Overview

Dataset statistics

Number of variables60
Number of observations3063
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory480.0 B

Variable types

Text18
Numeric27
Categorical15

Alerts

monster_number is highly overall correlated with monster_player_ratio and 2 other fieldsHigh correlation
monster_player_ratio is highly overall correlated with monster_number and 1 other fieldsHigh correlation
monster_total_level is highly overall correlated with party_total_level and 4 other fieldsHigh correlation
party_level1_spellslots is highly overall correlated with party_level2_spellslots and 14 other fieldsHigh correlation
party_level2_spellslots is highly overall correlated with party_level1_spellslots and 15 other fieldsHigh correlation
party_level3_spellslots is highly overall correlated with party_level1_spellslots and 8 other fieldsHigh correlation
party_level4_spellslots is highly overall correlated with party_level2_spellslots and 5 other fieldsHigh correlation
party_level5_spellslots is highly overall correlated with party_level3_spellslots and 6 other fieldsHigh correlation
party_level6_spellslots is highly overall correlated with party_level4_spellslots and 4 other fieldsHigh correlation
party_level7_spellslots is highly overall correlated with party_level5_spellslots and 3 other fieldsHigh correlation
party_level8_spellslots is highly overall correlated with party_level5_spellslots and 3 other fieldsHigh correlation
party_level9_spellslots is highly overall correlated with party_level5_spellslots and 3 other fieldsHigh correlation
party_size is highly overall correlated with party_level1_spellslots and 14 other fieldsHigh correlation
party_total_ac is highly overall correlated with party_level1_spellslots and 14 other fieldsHigh correlation
party_total_charisma is highly overall correlated with party_level1_spellslots and 13 other fieldsHigh correlation
party_total_constitution is highly overall correlated with party_level1_spellslots and 14 other fieldsHigh correlation
party_total_dexterity is highly overall correlated with party_level1_spellslots and 12 other fieldsHigh correlation
party_total_intelligence is highly overall correlated with party_level1_spellslots and 13 other fieldsHigh correlation
party_total_level is highly overall correlated with monster_total_level and 17 other fieldsHigh correlation
party_total_postcombat_hp is highly overall correlated with monster_total_level and 16 other fieldsHigh correlation
party_total_precombat_hp is highly overall correlated with monster_total_level and 16 other fieldsHigh correlation
party_total_prof_bonus is highly overall correlated with monster_total_level and 16 other fieldsHigh correlation
party_total_strength is highly overall correlated with party_level1_spellslots and 12 other fieldsHigh correlation
party_total_wisdom is highly overall correlated with party_level1_spellslots and 13 other fieldsHigh correlation
player_monster_ratio is highly overall correlated with monster_number and 1 other fieldsHigh correlation
weighted_monster_level is highly overall correlated with monster_number and 8 other fieldsHigh correlation
weighted_spell_slots is highly overall correlated with party_level1_spellslots and 14 other fieldsHigh correlation
party_level8_spellslots is highly imbalanced (86.1%)Imbalance
party_level9_spellslots is highly imbalanced (91.2%)Imbalance
Blood Hunter is highly imbalanced (62.1%)Imbalance
combat_id has unique valuesUnique
start_time has unique valuesUnique
monsters_info has unique valuesUnique
monster_total_level has 69 (2.3%) zerosZeros
party_level1_spellslots has 666 (21.7%) zerosZeros
party_level2_spellslots has 1021 (33.3%) zerosZeros
party_level3_spellslots has 1667 (54.4%) zerosZeros
party_level4_spellslots has 2201 (71.9%) zerosZeros
party_level5_spellslots has 2505 (81.8%) zerosZeros
party_level6_spellslots has 2768 (90.4%) zerosZeros
party_level7_spellslots has 2867 (93.6%) zerosZeros
weighted_monster_level has 69 (2.3%) zerosZeros
weighted_spell_slots has 542 (17.7%) zerosZeros

Reproduction

Analysis started2024-04-07 14:53:36.991472
Analysis finished2024-04-07 14:54:39.522410
Duration1 minute and 2.53 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

combat_id
Text

UNIQUE 

Distinct3063
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:39.615329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length47
Median length47
Mean length47
Min length47

Characters and Unicode

Total characters143961
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3063 ?
Unique (%)100.0%

Sample

1st row1663286431-4184e897-6036-4e34-825e-a4681bd884d9
2nd row1664579509-d9e3965d-ae0a-4500-8cf8-9950b4de88cd
3rd row1665260147-d44d239e-eb1a-4752-8cf9-c1e472d2eab1
4th row1667701925-338786ed-253b-48d3-9c83-91410316c82b
5th row1663810857-533116b6-85c4-41f6-8931-a1448345a3da
ValueCountFrequency (%)
1663286431-4184e897-6036-4e34-825e-a4681bd884d9 1
 
< 0.1%
1657360015-58f30ed3-9bda-41d0-b269-d30e21ba03c3 1
 
< 0.1%
1665260147-d44d239e-eb1a-4752-8cf9-c1e472d2eab1 1
 
< 0.1%
1667701925-338786ed-253b-48d3-9c83-91410316c82b 1
 
< 0.1%
1663810857-533116b6-85c4-41f6-8931-a1448345a3da 1
 
< 0.1%
1656747313-d2680df5-9c8a-4972-baf1-4728ab88dad4 1
 
< 0.1%
1658012043-e0da48bf-b3da-44a2-9ad2-d9ab5cd3e6b6 1
 
< 0.1%
1662135306-5f663cd2-cf15-4e41-b7d8-dbf1ca5a02c1 1
 
< 0.1%
1667701720-bb0a9408-2978-443b-920a-15089cfd9a7b 1
 
< 0.1%
1667067972-e07fb1c0-a0e7-4651-af77-1a7c9f6829a5 1
 
< 0.1%
Other values (3053) 3053
99.7%
2024-04-07T10:54:39.842271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 15315
 
10.6%
6 12871
 
8.9%
4 11018
 
7.7%
1 10880
 
7.6%
5 9079
 
6.3%
9 8667
 
6.0%
8 8647
 
6.0%
3 7988
 
5.5%
7 7920
 
5.5%
0 7842
 
5.4%
Other values (7) 43734
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143961
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 15315
 
10.6%
6 12871
 
8.9%
4 11018
 
7.7%
1 10880
 
7.6%
5 9079
 
6.3%
9 8667
 
6.0%
8 8647
 
6.0%
3 7988
 
5.5%
7 7920
 
5.5%
0 7842
 
5.4%
Other values (7) 43734
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143961
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 15315
 
10.6%
6 12871
 
8.9%
4 11018
 
7.7%
1 10880
 
7.6%
5 9079
 
6.3%
9 8667
 
6.0%
8 8647
 
6.0%
3 7988
 
5.5%
7 7920
 
5.5%
0 7842
 
5.4%
Other values (7) 43734
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143961
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 15315
 
10.6%
6 12871
 
8.9%
4 11018
 
7.7%
1 10880
 
7.6%
5 9079
 
6.3%
9 8667
 
6.0%
8 8647
 
6.0%
3 7988
 
5.5%
7 7920
 
5.5%
0 7842
 
5.4%
Other values (7) 43734
30.4%

start_time
Real number (ℝ)

UNIQUE 

Distinct3063
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6619347 × 109
Minimum1.6539245 × 109
Maximum1.669665 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:39.961904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1.6539245 × 109
5-th percentile1.6552209 × 109
Q11.6582463 × 109
median1.6619042 × 109
Q31.6656061 × 109
95-th percentile1.6687297 × 109
Maximum1.669665 × 109
Range15740482
Interquartile range (IQR)7359770.9

Descriptive statistics

Standard deviation4326306
Coefficient of variation (CV)0.0026031744
Kurtosis-1.1710367
Mean1.6619347 × 109
Median Absolute Deviation (MAD)3686783.8
Skewness0.028782005
Sum5.0905061 × 1012
Variance1.8716923 × 1013
MonotonicityNot monotonic
2024-04-07T10:54:40.073186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1663286431 1
 
< 0.1%
1665048960 1
 
< 0.1%
1655442497 1
 
< 0.1%
1667114172 1
 
< 0.1%
1665869684 1
 
< 0.1%
1669236107 1
 
< 0.1%
1656969699 1
 
< 0.1%
1664603969 1
 
< 0.1%
1659705904 1
 
< 0.1%
1662491295 1
 
< 0.1%
Other values (3053) 3053
99.7%
ValueCountFrequency (%)
1653924516 1
< 0.1%
1653933995 1
< 0.1%
1653934695 1
< 0.1%
1653958534 1
< 0.1%
1653968548 1
< 0.1%
1653971141 1
< 0.1%
1653997500 1
< 0.1%
1654010324 1
< 0.1%
1654036827 1
< 0.1%
1654049100 1
< 0.1%
ValueCountFrequency (%)
1669664998 1
< 0.1%
1669658359 1
< 0.1%
1669655923 1
< 0.1%
1669655374 1
< 0.1%
1669651736 1
< 0.1%
1669640610 1
< 0.1%
1669617061 1
< 0.1%
1669609711 1
< 0.1%
1669606208 1
< 0.1%
1669604776 1
< 0.1%
Distinct1810
Distinct (%)59.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:40.190705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length198
Median length176
Mean length53.495266
Min length22

Characters and Unicode

Total characters163856
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1493 ?
Unique (%)48.7%

Sample

1st row['171301054641998890', '227204640385900092', '131491822985054365']
2nd row['153068692087943643', '254235062041722309', '266753248612443040', '252465523787953538', '433213981677296135', '250953387656881590', '216435879949543624']
3rd row['299624821276559746', '211848255772817240', '164390109204685913']
4th row['717420956987500614']
5th row['196558367709637569', '499077502810776283', '268339945140599379']
ValueCountFrequency (%)
196058676817416704 120
 
1.6%
583320224080630228 117
 
1.6%
196558367709637569 109
 
1.5%
483720663430960073 107
 
1.4%
268339945140599379 78
 
1.0%
163432491052587407 75
 
1.0%
640172978900337268 74
 
1.0%
156982178525322073 68
 
0.9%
327601077009165698 64
 
0.9%
321444462285149813 64
 
0.9%
Other values (1312) 6572
88.2%
2024-04-07T10:54:40.422177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 15384
9.4%
' 14896
9.1%
1 14788
9.0%
7 14102
8.6%
3 13937
8.5%
6 13244
8.1%
8 12877
7.9%
0 12767
7.8%
4 12559
7.7%
5 12313
7.5%
Other values (5) 26989
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 163856
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 15384
9.4%
' 14896
9.1%
1 14788
9.0%
7 14102
8.6%
3 13937
8.5%
6 13244
8.1%
8 12877
7.9%
0 12767
7.8%
4 12559
7.7%
5 12313
7.5%
Other values (5) 26989
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 163856
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 15384
9.4%
' 14896
9.1%
1 14788
9.0%
7 14102
8.6%
3 13937
8.5%
6 13244
8.1%
8 12877
7.9%
0 12767
7.8%
4 12559
7.7%
5 12313
7.5%
Other values (5) 26989
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 163856
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 15384
9.4%
' 14896
9.1%
1 14788
9.0%
7 14102
8.6%
3 13937
8.5%
6 13244
8.1%
8 12877
7.9%
0 12767
7.8%
4 12559
7.7%
5 12313
7.5%
Other values (5) 26989
16.5%
Distinct3016
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:40.595005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length3668
Median length2562
Mean length906.9001
Min length356

Characters and Unicode

Total characters2777835
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2979 ?
Unique (%)97.3%

Sample

1st row[{'hp_ratio': (1, 85), 'class': [('Wizard', 10)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 13, 'stats': {'prof_bonus': 4, 'strength': 12, 'dexterity': 14, 'constitution': 19, 'intelligence': 20, 'wisdom': 13, 'charisma': 13}}, {'hp_ratio': None, 'class': [('Wizard', 11)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}, 'ac': 13, 'stats': {'prof_bonus': 4, 'strength': 11, 'dexterity': 17, 'constitution': 19, 'intelligence': 20, 'wisdom': 12, 'charisma': 11}}, {'hp_ratio': None, 'class': [('Bard', 5)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 14, 'stats': {'prof_bonus': 3, 'strength': 11, 'dexterity': 16, 'constitution': 16, 'intelligence': 11, 'wisdom': 12, 'charisma': 20}}, {'hp_ratio': None, 'class': [('Monk', 3)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 2, 'strength': 12, 'dexterity': 18, 'constitution': 14, 'intelligence': 12, 'wisdom': 18, 'charisma': 9}}]
2nd row[{'hp_ratio': None, 'class': [('Barbarian', 5)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 3, 'strength': 19, 'dexterity': 16, 'constitution': 18, 'intelligence': 14, 'wisdom': 16, 'charisma': 13}}, {'hp_ratio': None, 'class': [('Druid', 4)], 'slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 2, 'strength': 12, 'dexterity': 16, 'constitution': 16, 'intelligence': 13, 'wisdom': 20, 'charisma': 14}}, {'hp_ratio': None, 'class': [('Fighter', 3)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 2, 'strength': 14, 'dexterity': 19, 'constitution': 16, 'intelligence': 16, 'wisdom': 16, 'charisma': 16}}, {'hp_ratio': None, 'class': [('Cleric', 6)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 10, 'dexterity': 15, 'constitution': 16, 'intelligence': 13, 'wisdom': 20, 'charisma': 19}}, {'hp_ratio': None, 'class': [('Blood Hunter', 2), ('Cleric', 1)], 'slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 2, 'strength': 12, 'dexterity': 19, 'constitution': 16, 'intelligence': 13, 'wisdom': 18, 'charisma': 15}}, {'hp_ratio': None, 'class': [('Ranger', 6)], 'slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 20, 'stats': {'prof_bonus': 3, 'strength': 13, 'dexterity': 20, 'constitution': 20, 'intelligence': 14, 'wisdom': 16, 'charisma': 15}}, {'hp_ratio': (1, 98), 'class': [('Barbarian', 5), ('Fighter', 3)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 18, 'dexterity': 16, 'constitution': 20, 'intelligence': 13, 'wisdom': 13, 'charisma': 12}}]
3rd row[{'hp_ratio': None, 'class': [('Barbarian', 8)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 3, 'strength': 18, 'dexterity': 15, 'constitution': 18, 'intelligence': 5, 'wisdom': 12, 'charisma': 12}}, {'hp_ratio': (1, 60), 'class': [('Paladin', 7)], 'slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 3, 'strength': 10, 'dexterity': 18, 'constitution': 14, 'intelligence': 11, 'wisdom': 12, 'charisma': 14}}, {'hp_ratio': (0, 67), 'class': [('Fighter', 5), ('Paladin', 2)], 'slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 3, 'strength': 18, 'dexterity': 13, 'constitution': 17, 'intelligence': 8, 'wisdom': 10, 'charisma': 13}}]
4th row[{'hp_ratio': (1, 130), 'class': [('Wizard', 4), ('Barbarian', 3), ('Cleric', 1), ('Druid', 6)], 'slots': {'1': 4, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 5, 'strength': 18, 'dexterity': 18, 'constitution': 18, 'intelligence': 20, 'wisdom': 19, 'charisma': 18}}]
5th row[{'hp_ratio': (0, 37), 'class': [('Sorcerer', 5)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 15, 'stats': {'prof_bonus': 3, 'strength': 10, 'dexterity': 15, 'constitution': 15, 'intelligence': 12, 'wisdom': 13, 'charisma': 18}}, {'hp_ratio': (0, 59), 'class': [('Druid', 8)], 'slots': {'1': 3, '2': 2, '3': 1, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 15, 'stats': {'prof_bonus': 3, 'strength': 7, 'dexterity': 14, 'constitution': 14, 'intelligence': 13, 'wisdom': 20, 'charisma': 14}}, {'hp_ratio': (1, 77), 'class': [('Barbarian', 8)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 3, 'strength': 20, 'dexterity': 19, 'constitution': 14, 'intelligence': 11, 'wisdom': 13, 'charisma': 11}}]
ValueCountFrequency (%)
0 110386
23.6%
3 30640
 
6.5%
2 24725
 
5.3%
4 23767
 
5.1%
1 19935
 
4.3%
8 18647
 
4.0%
5 17667
 
3.8%
9 17348
 
3.7%
6 17012
 
3.6%
7 16305
 
3.5%
Other values (268) 171384
36.6%
2024-04-07T10:54:40.882695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 490638
17.7%
464753
16.7%
: 234918
 
8.5%
, 229601
 
8.3%
0 121090
 
4.4%
t 100268
 
3.6%
s 98514
 
3.5%
1 72179
 
2.6%
i 66263
 
2.4%
o 64939
 
2.3%
Other values (45) 834672
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2777835
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 490638
17.7%
464753
16.7%
: 234918
 
8.5%
, 229601
 
8.3%
0 121090
 
4.4%
t 100268
 
3.6%
s 98514
 
3.5%
1 72179
 
2.6%
i 66263
 
2.4%
o 64939
 
2.3%
Other values (45) 834672
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2777835
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 490638
17.7%
464753
16.7%
: 234918
 
8.5%
, 229601
 
8.3%
0 121090
 
4.4%
t 100268
 
3.6%
s 98514
 
3.5%
1 72179
 
2.6%
i 66263
 
2.4%
o 64939
 
2.3%
Other values (45) 834672
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2777835
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 490638
17.7%
464753
16.7%
: 234918
 
8.5%
, 229601
 
8.3%
0 121090
 
4.4%
t 100268
 
3.6%
s 98514
 
3.5%
1 72179
 
2.6%
i 66263
 
2.4%
o 64939
 
2.3%
Other values (45) 834672
30.0%

monsters_info
Text

UNIQUE 

Distinct3063
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:41.035503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length23938
Median length2902
Mean length573.59582
Min length116

Characters and Unicode

Total characters1756924
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3063 ?
Unique (%)100.0%

Sample

1st row[{'monster_id': '1a9bc800-94fd-49f7-a272-f697bc55afd0', 'monster_code': 'beholdy', 'monster_name': 'Aberrant Spirit', 'level': 0.125}]
2nd row[{'monster_id': '65e44831-1349-44de-8581-27792a3e074b', 'monster_code': 'BA3', 'monster_name': 'Basilisk', 'level': 3.0}, {'monster_id': '2c97d1f5-98ab-44cb-882a-fc231e12f65a', 'monster_code': 'BA2', 'monster_name': 'Basilisk', 'level': 3.0}, {'monster_id': 'b0ed09f3-fa4f-4fd0-8d8a-55041cebd30c', 'monster_code': 'BA1', 'monster_name': 'Basilisk', 'level': 3.0}, {'monster_id': '5d53b9dd-99de-47b9-8d9a-3077edb0051e', 'monster_code': 'THC1', 'monster_name': 'Two-Headed Cerberus', 'level': 2.0}, {'monster_id': '5f041113-8822-4d6a-9b5d-40e66167cfec', 'monster_code': 'GO1', 'monster_name': 'Gorgon', 'level': 5.0}, {'monster_id': '4c80ded9-f236-4fcd-8eac-fb9d91567eb4', 'monster_code': 'SH2', 'monster_name': 'Setessan Hoplite', 'level': 4.0}, {'monster_id': 'bf133f4b-d6b3-4a90-8bb5-24bc47ba6b6a', 'monster_code': 'SH1', 'monster_name': 'Setessan Hoplite', 'level': 4.0}, {'monster_id': 'f68dc49b-aae3-4c25-8be5-64b396277021', 'monster_code': 'SH3', 'monster_name': 'Setessan Hoplite', 'level': 4.0}, {'monster_id': 'f8b07182-a6f3-4ce3-9e7d-6cc041899d9b', 'monster_code': 'MH1', 'monster_name': 'Meletian Hoplite', 'level': 3.0}, {'monster_id': 'e510ce1d-1ac6-4c13-b483-2cd64f16b1fa', 'monster_code': 'DS1', 'monster_name': 'Duergar Spy', 'level': 2.0}, {'monster_id': 'cf5c5615-b703-424d-9057-03339ad3925c', 'monster_code': 'LC1', 'monster_name': 'Living Cloudkill', 'level': 7.0}, {'monster_id': 'c513e8d6-3611-4ac0-a9cc-f2c2a240cc83', 'monster_code': 'DK1', 'monster_name': 'Death Knight', 'level': 17.0}]
3rd row[{'monster_id': '7bb973b3-fd06-4b55-86b1-b51240798b17', 'monster_code': 'Orb', 'monster_name': 'Hoard Mimic', 'level': 8.0}]
4th row[{'monster_id': 'c106f013-e76e-4916-ada3-ca0a7d704bcd', 'monster_code': 'WE1', 'monster_name': 'Weretiger', 'level': 4.0}, {'monster_id': 'a9ac4210-8aae-45e2-9d63-f7aca9e51b0e', 'monster_code': 'TD1', 'monster_name': 'Tortle Druid', 'level': 2.0}, {'monster_id': '275cfd9a-a682-4309-873c-169ba4cb1e9b', 'monster_code': 'TD2', 'monster_name': 'Tortle Druid', 'level': 2.0}, {'monster_id': 'cd5ec6b3-80ca-42df-8a6d-ab9495e1b9f1', 'monster_code': 'TD3', 'monster_name': 'Tortle Druid', 'level': 2.0}]
5th row[{'monster_id': '2347b891-f809-42d0-954d-c567ba4686c4', 'monster_code': 'GR4', 'monster_name': 'Griffon', 'level': 2.0}, {'monster_id': 'fc97d553-09ab-42f0-b2dd-764acf776241', 'monster_code': 'GR3', 'monster_name': 'Griffon', 'level': 2.0}, {'monster_id': 'f8c178a8-0460-412a-abf7-c2cd81ec1098', 'monster_code': 'GR1', 'monster_name': 'Griffon', 'level': 2.0}, {'monster_id': 'de525814-ecd9-47f4-8f02-201e1bda082a', 'monster_code': 'GR2', 'monster_name': 'Griffon', 'level': 2.0}, {'monster_id': '7ffb5f52-4407-4b72-93f7-b4a87ecd2b12', 'monster_code': 'GR1', 'monster_name': 'Griffon', 'level': 2.0}, {'monster_id': '5ffa5e76-79b5-439d-9cf0-612c2ecaf836', 'monster_code': 'GR2', 'monster_name': 'Griffon', 'level': 2.0}, {'monster_id': '447b4956-b5cb-4415-91a3-5fbb515ac819', 'monster_code': 'GR4', 'monster_name': 'Griffon', 'level': 2.0}, {'monster_id': '98436900-bbc9-4715-8a0c-60866cc8de51', 'monster_code': 'GR3', 'monster_name': 'Griffon', 'level': 2.0}, {'monster_id': '71b7d144-9a87-411d-a4a4-daaf9b117008', 'monster_code': 'FS1', 'monster_name': 'Flying Snek', 'level': 2.0}, {'monster_id': '35039f8f-fc3e-4875-a70e-d0f832f561aa', 'monster_code': 'SA2', 'monster_name': 'Sahuagin', 'level': 0.5}, {'monster_id': '293b0712-449d-43eb-9906-d83e8609ad97', 'monster_code': 'SA5', 'monster_name': 'Sahuagin', 'level': 0.5}, {'monster_id': 'f82aa8e0-1c17-4db5-a2e0-de9c8fea1dac', 'monster_code': 'SA1', 'monster_name': 'Sahuagin', 'level': 0.5}, {'monster_id': '407f49a5-e93d-4f5e-8ddc-5fe5d0bb6cb2', 'monster_code': 'SA7', 'monster_name': 'Sahuagin', 'level': 0.5}, {'monster_id': '58a1c521-8d5d-4402-9d3c-b45dda821171', 'monster_code': 'SA4', 'monster_name': 'Sahuagin', 'level': 0.5}, {'monster_id': '9393b61c-21f4-4e6f-8740-780306a85dff', 'monster_code': 'SA8', 'monster_name': 'Sahuagin', 'level': 0.5}, {'monster_id': '6095bbee-bc93-4915-99a6-4806e5c7c31b', 'monster_code': 'SA3', 'monster_name': 'Sahuagin', 'level': 0.5}, {'monster_id': 'f8fd17e6-4304-4e55-9590-436ee2a8edc2', 'monster_code': 'SA6', 'monster_name': 'Sahuagin', 'level': 0.5}, {'monster_id': 'c606fcae-797c-4e31-9abb-06c88217946d', 'monster_code': 'ME4', 'monster_name': 'Merrow', 'level': 2.0}, {'monster_id': 'cd782ded-c617-4a75-8475-aa8bbdc5fccc', 'monster_code': 'ME5', 'monster_name': 'Merrow', 'level': 2.0}, {'monster_id': 'cd4e52c7-6812-4430-9e83-248e860fedac', 'monster_code': 'ME1', 'monster_name': 'Merrow', 'level': 2.0}, {'monster_id': 'e0d7bbcd-9633-4a28-abd6-02e8d9b110f6', 'monster_code': 'ME3', 'monster_name': 'Merrow', 'level': 2.0}, {'monster_id': 'ec3cb8f0-d084-47e5-8152-66112c9cb369', 'monster_code': 'ME6', 'monster_name': 'Merrow', 'level': 2.0}, {'monster_id': 'bc0d6f9f-1b27-4a37-b505-463397dd5ad0', 'monster_code': 'ME2', 'monster_name': 'Merrow', 'level': 2.0}, {'monster_id': 'dd9f4ecb-47a1-4e19-9d77-9081b03ee601', 'monster_code': 'ME7', 'monster_name': 'Merrow', 'level': 2.0}]
ValueCountFrequency (%)
level 14151
 
11.6%
monster_id 14139
 
11.6%
monster_code 14139
 
11.6%
monster_name 14139
 
11.6%
0.25 1793
 
1.5%
2.0 1599
 
1.3%
1.0 1392
 
1.1%
0.5 1379
 
1.1%
0.125 1357
 
1.1%
5.0 1084
 
0.9%
Other values (19360) 57090
46.7%
2024-04-07T10:54:41.311058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 197870
 
11.3%
e 139143
 
7.9%
119201
 
6.8%
o 67534
 
3.8%
n 65874
 
3.7%
m 59299
 
3.4%
d 58383
 
3.3%
a 57299
 
3.3%
- 56881
 
3.2%
: 56561
 
3.2%
Other values (85) 878879
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1756924
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 197870
 
11.3%
e 139143
 
7.9%
119201
 
6.8%
o 67534
 
3.8%
n 65874
 
3.7%
m 59299
 
3.4%
d 58383
 
3.3%
a 57299
 
3.3%
- 56881
 
3.2%
: 56561
 
3.2%
Other values (85) 878879
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1756924
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 197870
 
11.3%
e 139143
 
7.9%
119201
 
6.8%
o 67534
 
3.8%
n 65874
 
3.7%
m 59299
 
3.4%
d 58383
 
3.3%
a 57299
 
3.3%
- 56881
 
3.2%
: 56561
 
3.2%
Other values (85) 878879
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1756924
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 197870
 
11.3%
e 139143
 
7.9%
119201
 
6.8%
o 67534
 
3.8%
n 65874
 
3.7%
m 59299
 
3.4%
d 58383
 
3.3%
a 57299
 
3.3%
- 56881
 
3.2%
: 56561
 
3.2%
Other values (85) 878879
50.0%

party_size
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.431603
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:41.408036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6467434
Coefficient of variation (CV)0.67722543
Kurtosis-0.31892334
Mean2.431603
Median Absolute Deviation (MAD)1
Skewness0.88142543
Sum7448
Variance2.7117638
MonotonicityNot monotonic
2024-04-07T10:54:41.488853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 1362
44.5%
2 528
 
17.2%
4 359
 
11.7%
5 346
 
11.3%
3 329
 
10.7%
6 105
 
3.4%
7 27
 
0.9%
8 5
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
1 1362
44.5%
2 528
 
17.2%
3 329
 
10.7%
4 359
 
11.7%
5 346
 
11.3%
6 105
 
3.4%
7 27
 
0.9%
8 5
 
0.2%
9 2
 
0.1%
ValueCountFrequency (%)
9 2
 
0.1%
8 5
 
0.2%
7 27
 
0.9%
6 105
 
3.4%
5 346
 
11.3%
4 359
 
11.7%
3 329
 
10.7%
2 528
 
17.2%
1 1362
44.5%
Distinct948
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:41.559936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length77
Median length72
Mean length72.256611
Min length72

Characters and Unicode

Total characters221322
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique726 ?
Unique (%)23.7%

Sample

1st row{'1': 12, '2': 9, '3': 8, '4': 6, '5': 4, '6': 1, '7': 0, '8': 0, '9': 0}
2nd row{'1': 14, '2': 7, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
3rd row{'1': 6, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
4th row{'1': 4, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
5th row{'1': 7, '2': 5, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
ValueCountFrequency (%)
0 19574
35.5%
3 4981
 
9.0%
2 4457
 
8.1%
1 4103
 
7.4%
4 4080
 
7.4%
6 3601
 
6.5%
5 3521
 
6.4%
8 3455
 
6.3%
7 3304
 
6.0%
9 3272
 
5.9%
Other values (22) 786
 
1.4%
2024-04-07T10:54:41.722699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 55134
24.9%
52071
23.5%
: 27567
12.5%
, 24504
11.1%
0 19727
 
8.9%
3 5031
 
2.3%
1 4989
 
2.3%
2 4681
 
2.1%
4 4155
 
1.9%
6 3657
 
1.7%
Other values (6) 19806
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 221322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 55134
24.9%
52071
23.5%
: 27567
12.5%
, 24504
11.1%
0 19727
 
8.9%
3 5031
 
2.3%
1 4989
 
2.3%
2 4681
 
2.1%
4 4155
 
1.9%
6 3657
 
1.7%
Other values (6) 19806
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 221322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 55134
24.9%
52071
23.5%
: 27567
12.5%
, 24504
11.1%
0 19727
 
8.9%
3 5031
 
2.3%
1 4989
 
2.3%
2 4681
 
2.1%
4 4155
 
1.9%
6 3657
 
1.7%
Other values (6) 19806
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 221322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 55134
24.9%
52071
23.5%
: 27567
12.5%
, 24504
11.1%
0 19727
 
8.9%
3 5031
 
2.3%
1 4989
 
2.3%
2 4681
 
2.1%
4 4155
 
1.9%
6 3657
 
1.7%
Other values (6) 19806
 
8.9%
Distinct742
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:41.801699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length77
Median length72
Mean length72.296441
Min length72

Characters and Unicode

Total characters221444
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique497 ?
Unique (%)16.2%

Sample

1st row{'1': 12, '2': 9, '3': 8, '4': 6, '5': 4, '6': 1, '7': 0, '8': 0, '9': 0}
2nd row{'1': 14, '2': 8, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
3rd row{'1': 6, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
4th row{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}
5th row{'1': 8, '2': 6, '3': 5, '4': 2, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
ValueCountFrequency (%)
0 19241
34.9%
3 5205
 
9.4%
2 4374
 
7.9%
4 4140
 
7.5%
1 3944
 
7.2%
6 3698
 
6.7%
8 3556
 
6.4%
5 3501
 
6.3%
9 3314
 
6.0%
7 3253
 
5.9%
Other values (22) 908
 
1.6%
2024-04-07T10:54:41.964548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 55134
24.9%
52071
23.5%
: 27567
12.4%
, 24504
11.1%
0 19386
 
8.8%
3 5264
 
2.4%
1 4947
 
2.2%
2 4679
 
2.1%
4 4222
 
1.9%
6 3775
 
1.7%
Other values (6) 19895
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 221444
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 55134
24.9%
52071
23.5%
: 27567
12.4%
, 24504
11.1%
0 19386
 
8.8%
3 5264
 
2.4%
1 4947
 
2.2%
2 4679
 
2.1%
4 4222
 
1.9%
6 3775
 
1.7%
Other values (6) 19895
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 221444
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 55134
24.9%
52071
23.5%
: 27567
12.4%
, 24504
11.1%
0 19386
 
8.8%
3 5264
 
2.4%
1 4947
 
2.2%
2 4679
 
2.1%
4 4222
 
1.9%
6 3775
 
1.7%
Other values (6) 19895
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 221444
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 55134
24.9%
52071
23.5%
: 27567
12.4%
, 24504
11.1%
0 19386
 
8.8%
3 5264
 
2.4%
1 4947
 
2.2%
2 4679
 
2.1%
4 4222
 
1.9%
6 3775
 
1.7%
Other values (6) 19895
 
9.0%
Distinct2176
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:42.064970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length208
Median length170
Mean length53.104799
Min length13

Characters and Unicode

Total characters162660
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1900 ?
Unique (%)62.0%

Sample

1st row[('Wizard', 10), ('Wizard', 11), ('Bard', 5), ('Monk', 3)]
2nd row[('Barbarian', 5), ('Druid', 4), ('Fighter', 3), ('Cleric', 6), ('Blood Hunter', 2), ('Cleric', 1), ('Ranger', 6), ('Barbarian', 5), ('Fighter', 3)]
3rd row[('Barbarian', 8), ('Paladin', 7), ('Fighter', 5), ('Paladin', 2)]
4th row[('Wizard', 4), ('Barbarian', 3), ('Cleric', 1), ('Druid', 6)]
5th row[('Sorcerer', 5), ('Druid', 8), ('Barbarian', 8)]
ValueCountFrequency (%)
fighter 1522
 
7.2%
3 1430
 
6.8%
5 1370
 
6.5%
1 1346
 
6.4%
4 1289
 
6.1%
2 1268
 
6.0%
warlock 1076
 
5.1%
rogue 1061
 
5.0%
cleric 998
 
4.7%
6 964
 
4.6%
Other values (25) 8712
41.4%
2024-04-07T10:54:42.286359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 20802
 
12.8%
17973
 
11.0%
, 17739
 
10.9%
) 10401
 
6.4%
( 10401
 
6.4%
r 10294
 
6.3%
a 7465
 
4.6%
e 5867
 
3.6%
i 5639
 
3.5%
o 3960
 
2.4%
Other values (33) 52119
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 162660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 20802
 
12.8%
17973
 
11.0%
, 17739
 
10.9%
) 10401
 
6.4%
( 10401
 
6.4%
r 10294
 
6.3%
a 7465
 
4.6%
e 5867
 
3.6%
i 5639
 
3.5%
o 3960
 
2.4%
Other values (33) 52119
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 162660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 20802
 
12.8%
17973
 
11.0%
, 17739
 
10.9%
) 10401
 
6.4%
( 10401
 
6.4%
r 10294
 
6.3%
a 7465
 
4.6%
e 5867
 
3.6%
i 5639
 
3.5%
o 3960
 
2.4%
Other values (33) 52119
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 162660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 20802
 
12.8%
17973
 
11.0%
, 17739
 
10.9%
) 10401
 
6.4%
( 10401
 
6.4%
r 10294
 
6.3%
a 7465
 
4.6%
e 5867
 
3.6%
i 5639
 
3.5%
o 3960
 
2.4%
Other values (33) 52119
32.0%
Distinct1573
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:42.378254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length140
Median length114
Mean length35.714985
Min length8

Characters and Unicode

Total characters109395
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1376 ?
Unique (%)44.9%

Sample

1st row['Wizard', 'Wizard', 'Bard', 'Monk']
2nd row['Barbarian', 'Druid', 'Fighter', 'Cleric', 'Blood Hunter', 'Cleric', 'Ranger', 'Barbarian', 'Fighter']
3rd row['Barbarian', 'Paladin', 'Fighter', 'Paladin']
4th row['Wizard', 'Barbarian', 'Cleric', 'Druid']
5th row['Sorcerer', 'Druid', 'Barbarian']
ValueCountFrequency (%)
fighter 1522
14.3%
warlock 1076
10.1%
rogue 1061
10.0%
cleric 998
9.4%
wizard 919
8.6%
barbarian 898
8.4%
paladin 871
8.2%
sorcerer 762
7.2%
monk 597
 
5.6%
ranger 530
 
5.0%
Other values (4) 1399
13.2%
2024-04-07T10:54:42.664346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 20802
19.0%
r 10294
 
9.4%
7570
 
6.9%
a 7465
 
6.8%
, 7338
 
6.7%
e 5867
 
5.4%
i 5639
 
5.2%
o 3960
 
3.6%
l 3177
 
2.9%
n 3128
 
2.9%
Other values (21) 34155
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109395
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 20802
19.0%
r 10294
 
9.4%
7570
 
6.9%
a 7465
 
6.8%
, 7338
 
6.7%
e 5867
 
5.4%
i 5639
 
5.2%
o 3960
 
3.6%
l 3177
 
2.9%
n 3128
 
2.9%
Other values (21) 34155
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109395
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 20802
19.0%
r 10294
 
9.4%
7570
 
6.9%
a 7465
 
6.8%
, 7338
 
6.7%
e 5867
 
5.4%
i 5639
 
5.2%
o 3960
 
3.6%
l 3177
 
2.9%
n 3128
 
2.9%
Other values (21) 34155
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109395
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 20802
19.0%
r 10294
 
9.4%
7570
 
6.9%
a 7465
 
6.8%
, 7338
 
6.7%
e 5867
 
5.4%
i 5639
 
5.2%
o 3960
 
3.6%
l 3177
 
2.9%
n 3128
 
2.9%
Other values (21) 34155
31.2%
Distinct2703
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:42.851416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length103
Median length81
Mean length23.497225
Min length8

Characters and Unicode

Total characters71972
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2468 ?
Unique (%)80.6%

Sample

1st row[(1, 85)]
2nd row[(1, 98)]
3rd row[(1, 60), (0, 67)]
4th row[(1, 130)]
5th row[(0, 37), (0, 59), (1, 77)]
ValueCountFrequency (%)
0 545
 
3.8%
38 297
 
2.1%
27 295
 
2.0%
31 289
 
2.0%
24 287
 
2.0%
44 271
 
1.9%
43 233
 
1.6%
52 226
 
1.6%
45 223
 
1.5%
20 222
 
1.5%
Other values (239) 11558
80.0%
2024-04-07T10:54:43.197415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 11383
15.8%
11383
15.8%
( 7223
10.0%
) 7223
10.0%
1 4750
 
6.6%
3 3781
 
5.3%
2 3727
 
5.2%
4 3434
 
4.8%
[ 3063
 
4.3%
] 3063
 
4.3%
Other values (6) 12942
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 11383
15.8%
11383
15.8%
( 7223
10.0%
) 7223
10.0%
1 4750
 
6.6%
3 3781
 
5.3%
2 3727
 
5.2%
4 3434
 
4.8%
[ 3063
 
4.3%
] 3063
 
4.3%
Other values (6) 12942
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 11383
15.8%
11383
15.8%
( 7223
10.0%
) 7223
10.0%
1 4750
 
6.6%
3 3781
 
5.3%
2 3727
 
5.2%
4 3434
 
4.8%
[ 3063
 
4.3%
] 3063
 
4.3%
Other values (6) 12942
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 11383
15.8%
11383
15.8%
( 7223
10.0%
) 7223
10.0%
1 4750
 
6.6%
3 3781
 
5.3%
2 3727
 
5.2%
4 3434
 
4.8%
[ 3063
 
4.3%
] 3063
 
4.3%
Other values (6) 12942
18.0%
Distinct1310
Distinct (%)42.8%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:43.298736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length40
Median length36
Mean length9.8942214
Min length3

Characters and Unicode

Total characters30306
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1138 ?
Unique (%)37.2%

Sample

1st row[13, 13, 14, 18]
2nd row[17, 16, 16, 19, 16, 20, 19]
3rd row[16, 16, 16]
4th row[19]
5th row[15, 15, 17]
ValueCountFrequency (%)
18 1323
17.5%
16 1063
14.0%
17 982
13.0%
15 743
9.8%
19 705
9.3%
14 633
8.4%
20 482
 
6.4%
13 387
 
5.1%
21 276
 
3.6%
12 267
 
3.5%
Other values (16) 717
9.5%
2024-04-07T10:54:43.499123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6727
22.2%
, 4515
14.9%
4515
14.9%
[ 3063
10.1%
] 3063
10.1%
2 1704
 
5.6%
8 1332
 
4.4%
6 1082
 
3.6%
7 993
 
3.3%
5 761
 
2.5%
Other values (4) 2551
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30306
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6727
22.2%
, 4515
14.9%
4515
14.9%
[ 3063
10.1%
] 3063
10.1%
2 1704
 
5.6%
8 1332
 
4.4%
6 1082
 
3.6%
7 993
 
3.3%
5 761
 
2.5%
Other values (4) 2551
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30306
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6727
22.2%
, 4515
14.9%
4515
14.9%
[ 3063
10.1%
] 3063
10.1%
2 1704
 
5.6%
8 1332
 
4.4%
6 1082
 
3.6%
7 993
 
3.3%
5 761
 
2.5%
Other values (4) 2551
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30306
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6727
22.2%
, 4515
14.9%
4515
14.9%
[ 3063
10.1%
] 3063
10.1%
2 1704
 
5.6%
8 1332
 
4.4%
6 1082
 
3.6%
7 993
 
3.3%
5 761
 
2.5%
Other values (4) 2551
 
8.4%
Distinct364
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:43.567873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length30
Median length27
Mean length7.4221352
Min length3

Characters and Unicode

Total characters22734
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique225 ?
Unique (%)7.3%

Sample

1st row[4, 4, 3, 2]
2nd row[3, 2, 2, 3, 2, 3, 3]
3rd row[3, 3, 3]
4th row[5]
5th row[3, 3, 3]
ValueCountFrequency (%)
3 3310
43.7%
2 2130
28.1%
4 1225
 
16.2%
5 534
 
7.0%
6 368
 
4.9%
7 10
 
0.1%
1 1
 
< 0.1%
2024-04-07T10:54:43.725269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 4515
19.9%
4515
19.9%
3 3310
14.6%
[ 3063
13.5%
] 3063
13.5%
2 2130
9.4%
4 1225
 
5.4%
5 534
 
2.3%
6 368
 
1.6%
7 10
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
, 4515
19.9%
4515
19.9%
3 3310
14.6%
[ 3063
13.5%
] 3063
13.5%
2 2130
9.4%
4 1225
 
5.4%
5 534
 
2.3%
6 368
 
1.6%
7 10
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
, 4515
19.9%
4515
19.9%
3 3310
14.6%
[ 3063
13.5%
] 3063
13.5%
2 2130
9.4%
4 1225
 
5.4%
5 534
 
2.3%
6 368
 
1.6%
7 10
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
, 4515
19.9%
4515
19.9%
3 3310
14.6%
[ 3063
13.5%
] 3063
13.5%
2 2130
9.4%
4 1225
 
5.4%
5 534
 
2.3%
6 368
 
1.6%
7 10
 
< 0.1%
Distinct1339
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:43.809007image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length9.2843617
Min length3

Characters and Unicode

Total characters28438
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1167 ?
Unique (%)38.1%

Sample

1st row[12, 11, 11, 12]
2nd row[19, 12, 14, 10, 12, 13, 18]
3rd row[18, 10, 18]
4th row[18]
5th row[10, 7, 20]
ValueCountFrequency (%)
8 1001
13.2%
10 942
12.4%
18 645
8.5%
16 613
8.1%
9 578
7.6%
13 537
7.1%
20 532
7.0%
14 527
7.0%
11 485
 
6.4%
12 423
 
5.6%
Other values (11) 1295
17.1%
2024-04-07T10:54:44.013414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5647
19.9%
, 4515
15.9%
4515
15.9%
[ 3063
10.8%
] 3063
10.8%
8 1646
 
5.8%
0 1474
 
5.2%
2 1058
 
3.7%
9 928
 
3.3%
6 677
 
2.4%
Other values (4) 1852
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5647
19.9%
, 4515
15.9%
4515
15.9%
[ 3063
10.8%
] 3063
10.8%
8 1646
 
5.8%
0 1474
 
5.2%
2 1058
 
3.7%
9 928
 
3.3%
6 677
 
2.4%
Other values (4) 1852
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5647
19.9%
, 4515
15.9%
4515
15.9%
[ 3063
10.8%
] 3063
10.8%
8 1646
 
5.8%
0 1474
 
5.2%
2 1058
 
3.7%
9 928
 
3.3%
6 677
 
2.4%
Other values (4) 1852
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5647
19.9%
, 4515
15.9%
4515
15.9%
[ 3063
10.8%
] 3063
10.8%
8 1646
 
5.8%
0 1474
 
5.2%
2 1058
 
3.7%
9 928
 
3.3%
6 677
 
2.4%
Other values (4) 1852
 
6.5%
Distinct1286
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:44.100729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length39
Median length36
Mean length9.7851779
Min length3

Characters and Unicode

Total characters29972
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1133 ?
Unique (%)37.0%

Sample

1st row[14, 17, 16, 18]
2nd row[16, 16, 19, 15, 19, 20, 16]
3rd row[15, 18, 13]
4th row[18]
5th row[15, 14, 19]
ValueCountFrequency (%)
14 1408
18.6%
16 1156
15.3%
18 1024
13.5%
20 897
11.8%
12 519
 
6.8%
13 488
 
6.4%
15 442
 
5.8%
17 396
 
5.2%
10 336
 
4.4%
11 304
 
4.0%
Other values (9) 608
8.0%
2024-04-07T10:54:44.337640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6619
22.1%
, 4515
15.1%
4515
15.1%
[ 3063
10.2%
] 3063
10.2%
2 1471
 
4.9%
4 1408
 
4.7%
0 1233
 
4.1%
6 1195
 
4.0%
8 1182
 
3.9%
Other values (4) 1708
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29972
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6619
22.1%
, 4515
15.1%
4515
15.1%
[ 3063
10.2%
] 3063
10.2%
2 1471
 
4.9%
4 1408
 
4.7%
0 1233
 
4.1%
6 1195
 
4.0%
8 1182
 
3.9%
Other values (4) 1708
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29972
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6619
22.1%
, 4515
15.1%
4515
15.1%
[ 3063
10.2%
] 3063
10.2%
2 1471
 
4.9%
4 1408
 
4.7%
0 1233
 
4.1%
6 1195
 
4.0%
8 1182
 
3.9%
Other values (4) 1708
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29972
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6619
22.1%
, 4515
15.1%
4515
15.1%
[ 3063
10.2%
] 3063
10.2%
2 1471
 
4.9%
4 1408
 
4.7%
0 1233
 
4.1%
6 1195
 
4.0%
8 1182
 
3.9%
Other values (4) 1708
 
5.7%
Distinct1159
Distinct (%)37.8%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:44.437921image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length40
Median length36
Mean length9.88508
Min length3

Characters and Unicode

Total characters30278
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique976 ?
Unique (%)31.9%

Sample

1st row[19, 19, 16, 14]
2nd row[18, 16, 16, 16, 16, 20, 20]
3rd row[18, 14, 17]
4th row[18]
5th row[15, 14, 14]
ValueCountFrequency (%)
16 1771
23.4%
14 1700
22.4%
18 907
12.0%
15 783
10.3%
12 514
 
6.8%
13 512
 
6.8%
20 444
 
5.9%
17 392
 
5.2%
19 278
 
3.7%
10 156
 
2.1%
Other values (5) 121
 
1.6%
2024-04-07T10:54:44.657939image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7186
23.7%
, 4515
14.9%
4515
14.9%
[ 3063
10.1%
] 3063
10.1%
6 1771
 
5.8%
4 1700
 
5.6%
2 959
 
3.2%
8 922
 
3.0%
5 783
 
2.6%
Other values (4) 1801
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7186
23.7%
, 4515
14.9%
4515
14.9%
[ 3063
10.1%
] 3063
10.1%
6 1771
 
5.8%
4 1700
 
5.6%
2 959
 
3.2%
8 922
 
3.0%
5 783
 
2.6%
Other values (4) 1801
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7186
23.7%
, 4515
14.9%
4515
14.9%
[ 3063
10.1%
] 3063
10.1%
6 1771
 
5.8%
4 1700
 
5.6%
2 959
 
3.2%
8 922
 
3.0%
5 783
 
2.6%
Other values (4) 1801
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7186
23.7%
, 4515
14.9%
4515
14.9%
[ 3063
10.1%
] 3063
10.1%
6 1771
 
5.8%
4 1700
 
5.6%
2 959
 
3.2%
8 922
 
3.0%
5 783
 
2.6%
Other values (4) 1801
 
5.9%
Distinct1284
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:44.744681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length40
Median length32
Mean length9.4257264
Min length3

Characters and Unicode

Total characters28871
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1125 ?
Unique (%)36.7%

Sample

1st row[20, 20, 11, 12]
2nd row[14, 13, 16, 13, 13, 14, 13]
3rd row[5, 11, 8]
4th row[20]
5th row[12, 13, 11]
ValueCountFrequency (%)
10 1339
17.7%
12 986
13.0%
8 832
11.0%
11 827
10.9%
14 702
9.3%
13 572
7.5%
9 510
 
6.7%
16 418
 
5.5%
20 371
 
4.9%
18 335
 
4.4%
Other values (8) 686
9.1%
2024-04-07T10:54:44.943899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6581
22.8%
, 4515
15.6%
4515
15.6%
[ 3063
10.6%
] 3063
10.6%
0 1710
 
5.9%
2 1381
 
4.8%
8 1167
 
4.0%
9 765
 
2.6%
4 703
 
2.4%
Other values (4) 1408
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 28871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6581
22.8%
, 4515
15.6%
4515
15.6%
[ 3063
10.6%
] 3063
10.6%
0 1710
 
5.9%
2 1381
 
4.8%
8 1167
 
4.0%
9 765
 
2.6%
4 703
 
2.4%
Other values (4) 1408
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 28871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6581
22.8%
, 4515
15.6%
4515
15.6%
[ 3063
10.6%
] 3063
10.6%
0 1710
 
5.9%
2 1381
 
4.8%
8 1167
 
4.0%
9 765
 
2.6%
4 703
 
2.4%
Other values (4) 1408
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 28871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6581
22.8%
, 4515
15.6%
4515
15.6%
[ 3063
10.6%
] 3063
10.6%
0 1710
 
5.9%
2 1381
 
4.8%
8 1167
 
4.0%
9 765
 
2.6%
4 703
 
2.4%
Other values (4) 1408
 
4.9%
Distinct1284
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:45.095993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length40
Median length36
Mean length9.7930134
Min length3

Characters and Unicode

Total characters29996
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1127 ?
Unique (%)36.8%

Sample

1st row[13, 12, 12, 18]
2nd row[16, 20, 16, 20, 18, 16, 13]
3rd row[12, 12, 10]
4th row[19]
5th row[13, 20, 13]
ValueCountFrequency (%)
12 1074
14.2%
14 1065
14.1%
13 931
12.3%
10 911
12.0%
16 904
11.9%
11 566
7.5%
18 526
6.9%
20 481
6.3%
15 413
 
5.4%
17 259
 
3.4%
Other values (9) 448
5.9%
2024-04-07T10:54:45.302537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 7336
24.5%
, 4515
15.1%
4515
15.1%
[ 3063
10.2%
] 3063
10.2%
2 1579
 
5.3%
0 1392
 
4.6%
4 1071
 
3.6%
3 931
 
3.1%
6 920
 
3.1%
Other values (4) 1611
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29996
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 7336
24.5%
, 4515
15.1%
4515
15.1%
[ 3063
10.2%
] 3063
10.2%
2 1579
 
5.3%
0 1392
 
4.6%
4 1071
 
3.6%
3 931
 
3.1%
6 920
 
3.1%
Other values (4) 1611
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29996
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 7336
24.5%
, 4515
15.1%
4515
15.1%
[ 3063
10.2%
] 3063
10.2%
2 1579
 
5.3%
0 1392
 
4.6%
4 1071
 
3.6%
3 931
 
3.1%
6 920
 
3.1%
Other values (4) 1611
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29996
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 7336
24.5%
, 4515
15.1%
4515
15.1%
[ 3063
10.2%
] 3063
10.2%
2 1579
 
5.3%
0 1392
 
4.6%
4 1071
 
3.6%
3 931
 
3.1%
6 920
 
3.1%
Other values (4) 1611
 
5.4%
Distinct1321
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:45.394112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length39
Median length35
Mean length9.5076722
Min length3

Characters and Unicode

Total characters29122
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1170 ?
Unique (%)38.2%

Sample

1st row[13, 11, 20, 9]
2nd row[13, 14, 16, 19, 15, 15, 12]
3rd row[12, 14, 13]
4th row[18]
5th row[18, 14, 11]
ValueCountFrequency (%)
10 930
12.3%
20 791
10.4%
12 785
10.4%
14 758
10.0%
18 737
9.7%
8 670
8.8%
11 597
7.9%
13 564
7.4%
16 545
7.2%
9 376
5.0%
Other values (10) 825
10.9%
2024-04-07T10:54:45.598581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 6183
21.2%
, 4515
15.5%
4515
15.5%
[ 3063
10.5%
] 3063
10.5%
0 1721
 
5.9%
2 1599
 
5.5%
8 1407
 
4.8%
4 762
 
2.6%
6 580
 
2.0%
Other values (4) 1714
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 6183
21.2%
, 4515
15.5%
4515
15.5%
[ 3063
10.5%
] 3063
10.5%
0 1721
 
5.9%
2 1599
 
5.5%
8 1407
 
4.8%
4 762
 
2.6%
6 580
 
2.0%
Other values (4) 1714
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 6183
21.2%
, 4515
15.5%
4515
15.5%
[ 3063
10.5%
] 3063
10.5%
0 1721
 
5.9%
2 1599
 
5.5%
8 1407
 
4.8%
4 762
 
2.6%
6 580
 
2.0%
Other values (4) 1714
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 6183
21.2%
, 4515
15.5%
4515
15.5%
[ 3063
10.5%
] 3063
10.5%
0 1721
 
5.9%
2 1599
 
5.5%
8 1407
 
4.8%
4 762
 
2.6%
6 580
 
2.0%
Other values (4) 1714
 
5.9%
Distinct2168
Distinct (%)70.8%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:45.759656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length2644
Median length422
Mean length64.813908
Min length7

Characters and Unicode

Total characters198525
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1954 ?
Unique (%)63.8%

Sample

1st row['Aberrant Spirit']
2nd row['Basilisk', 'Basilisk', 'Basilisk', 'Two-Headed Cerberus', 'Gorgon', 'Setessan Hoplite', 'Setessan Hoplite', 'Setessan Hoplite', 'Meletian Hoplite', 'Duergar Spy', 'Living Cloudkill', 'Death Knight']
3rd row['Hoard Mimic']
4th row['Weretiger', 'Tortle Druid', 'Tortle Druid', 'Tortle Druid']
5th row['Griffon', 'Griffon', 'Griffon', 'Griffon', 'Griffon', 'Griffon', 'Griffon', 'Griffon', 'Flying Snek', 'Sahuagin', 'Sahuagin', 'Sahuagin', 'Sahuagin', 'Sahuagin', 'Sahuagin', 'Sahuagin', 'Sahuagin', 'Merrow', 'Merrow', 'Merrow', 'Merrow', 'Merrow', 'Merrow', 'Merrow']
ValueCountFrequency (%)
giant 1047
 
4.6%
wolf 488
 
2.2%
mage 456
 
2.0%
rat 441
 
2.0%
bandit 423
 
1.9%
of 366
 
1.6%
zombie 361
 
1.6%
goblin 319
 
1.4%
skeleton 296
 
1.3%
dragon 285
 
1.3%
Other values (1550) 18115
80.2%
2024-04-07T10:54:46.044881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 28227
14.2%
19536
 
9.8%
e 12206
 
6.1%
a 11690
 
5.9%
r 11217
 
5.7%
, 11098
 
5.6%
o 9797
 
4.9%
i 8846
 
4.5%
n 8527
 
4.3%
t 7943
 
4.0%
Other values (70) 69438
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 198525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
' 28227
14.2%
19536
 
9.8%
e 12206
 
6.1%
a 11690
 
5.9%
r 11217
 
5.7%
, 11098
 
5.6%
o 9797
 
4.9%
i 8846
 
4.5%
n 8527
 
4.3%
t 7943
 
4.0%
Other values (70) 69438
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 198525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
' 28227
14.2%
19536
 
9.8%
e 12206
 
6.1%
a 11690
 
5.9%
r 11217
 
5.7%
, 11098
 
5.6%
o 9797
 
4.9%
i 8846
 
4.5%
n 8527
 
4.3%
t 7943
 
4.0%
Other values (70) 69438
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 198525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
' 28227
14.2%
19536
 
9.8%
e 12206
 
6.1%
a 11690
 
5.9%
r 11217
 
5.7%
, 11098
 
5.6%
o 9797
 
4.9%
i 8846
 
4.5%
n 8527
 
4.3%
t 7943
 
4.0%
Other values (70) 69438
35.0%

monster_number
Real number (ℝ)

HIGH CORRELATION 

Distinct45
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6160627
Minimum1
Maximum194
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:46.166375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q36
95-th percentile15
Maximum194
Range193
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.1280279
Coefficient of variation (CV)1.5441792
Kurtosis198.09091
Mean4.6160627
Median Absolute Deviation (MAD)1
Skewness9.8512308
Sum14139
Variance50.808782
MonotonicityNot monotonic
2024-04-07T10:54:46.272792image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1 1198
39.1%
2 414
 
13.5%
3 255
 
8.3%
4 228
 
7.4%
5 172
 
5.6%
6 152
 
5.0%
7 97
 
3.2%
8 94
 
3.1%
9 72
 
2.4%
10 61
 
2.0%
Other values (35) 320
 
10.4%
ValueCountFrequency (%)
1 1198
39.1%
2 414
 
13.5%
3 255
 
8.3%
4 228
 
7.4%
5 172
 
5.6%
6 152
 
5.0%
7 97
 
3.2%
8 94
 
3.1%
9 72
 
2.4%
10 61
 
2.0%
ValueCountFrequency (%)
194 1
< 0.1%
118 1
< 0.1%
87 1
< 0.1%
74 1
< 0.1%
67 1
< 0.1%
61 1
< 0.1%
54 1
< 0.1%
53 1
< 0.1%
51 1
< 0.1%
42 2
0.1%

monster_total_level
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct320
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.3488
Minimum0
Maximum393.25
Zeros69
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:46.380357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q14
median8
Q318
95-th percentile51.25
Maximum393.25
Range393.25
Interquartile range (IQR)14

Descriptive statistics

Standard deviation23.340593
Coefficient of variation (CV)1.5206787
Kurtosis64.730799
Mean15.3488
Median Absolute Deviation (MAD)6
Skewness6.0361451
Sum47013.375
Variance544.78329
MonotonicityNot monotonic
2024-04-07T10:54:46.496300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 381
 
12.4%
2 128
 
4.2%
0.25 113
 
3.7%
9 107
 
3.5%
7 93
 
3.0%
5 89
 
2.9%
4 86
 
2.8%
1 83
 
2.7%
8 82
 
2.7%
3 73
 
2.4%
Other values (310) 1828
59.7%
ValueCountFrequency (%)
0 69
2.3%
0.125 25
 
0.8%
0.25 113
3.7%
0.375 11
 
0.4%
0.5 53
1.7%
0.625 4
 
0.1%
0.75 23
 
0.8%
0.875 4
 
0.1%
1 83
2.7%
1.125 1
 
< 0.1%
ValueCountFrequency (%)
393.25 1
< 0.1%
360 1
< 0.1%
322 1
< 0.1%
315.5 1
< 0.1%
206.125 1
< 0.1%
193.375 1
< 0.1%
181 1
< 0.1%
166.125 1
< 0.1%
159.5 1
< 0.1%
155 1
< 0.1%

party_total_level
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.728371
Minimum1
Maximum136
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:46.707192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q17
median13
Q324
95-th percentile50
Maximum136
Range135
Interquartile range (IQR)17

Descriptive statistics

Standard deviation15.684741
Coefficient of variation (CV)0.88472546
Kurtosis5.1493613
Mean17.728371
Median Absolute Deviation (MAD)7
Skewness1.9529574
Sum54302
Variance246.01111
MonotonicityNot monotonic
2024-04-07T10:54:46.815543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 210
 
6.9%
6 173
 
5.6%
3 172
 
5.6%
4 170
 
5.6%
8 164
 
5.4%
10 151
 
4.9%
7 150
 
4.9%
9 129
 
4.2%
12 101
 
3.3%
15 101
 
3.3%
Other values (83) 1542
50.3%
ValueCountFrequency (%)
1 9
 
0.3%
2 16
 
0.5%
3 172
5.6%
4 170
5.6%
5 210
6.9%
6 173
5.6%
7 150
4.9%
8 164
5.4%
9 129
4.2%
10 151
4.9%
ValueCountFrequency (%)
136 1
< 0.1%
117 1
< 0.1%
100 2
0.1%
98 1
< 0.1%
97 1
< 0.1%
94 1
< 0.1%
91 2
0.1%
89 1
< 0.1%
88 1
< 0.1%
87 2
0.1%

party_level1_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.144303
Minimum0
Maximum30
Zeros666
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:46.909229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q38
95-th percentile15
Maximum30
Range30
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6706072
Coefficient of variation (CV)0.90791837
Kurtosis1.2478139
Mean5.144303
Median Absolute Deviation (MAD)3
Skewness1.125555
Sum15757
Variance21.814572
MonotonicityNot monotonic
2024-04-07T10:54:47.001536image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
4 745
24.3%
0 666
21.7%
3 247
 
8.1%
8 240
 
7.8%
2 143
 
4.7%
7 133
 
4.3%
5 120
 
3.9%
12 114
 
3.7%
10 97
 
3.2%
6 94
 
3.1%
Other values (19) 464
15.1%
ValueCountFrequency (%)
0 666
21.7%
1 80
 
2.6%
2 143
 
4.7%
3 247
 
8.1%
4 745
24.3%
5 120
 
3.9%
6 94
 
3.1%
7 133
 
4.3%
8 240
 
7.8%
9 59
 
1.9%
ValueCountFrequency (%)
30 1
 
< 0.1%
29 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
23 2
 
0.1%
22 3
 
0.1%
21 6
0.2%
20 10
0.3%
19 9
0.3%

party_level2_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9823702
Minimum0
Maximum20
Zeros1021
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:47.089082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q35
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.1466989
Coefficient of variation (CV)1.0551
Kurtosis2.2288796
Mean2.9823702
Median Absolute Deviation (MAD)3
Skewness1.3656177
Sum9135
Variance9.9017139
MonotonicityNot monotonic
2024-04-07T10:54:47.179560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1021
33.3%
3 722
23.6%
2 385
 
12.6%
6 223
 
7.3%
5 175
 
5.7%
4 104
 
3.4%
8 96
 
3.1%
9 78
 
2.5%
1 64
 
2.1%
7 63
 
2.1%
Other values (10) 132
 
4.3%
ValueCountFrequency (%)
0 1021
33.3%
1 64
 
2.1%
2 385
 
12.6%
3 722
23.6%
4 104
 
3.4%
5 175
 
5.7%
6 223
 
7.3%
7 63
 
2.1%
8 96
 
3.1%
9 78
 
2.5%
ValueCountFrequency (%)
20 3
 
0.1%
18 1
 
< 0.1%
17 4
 
0.1%
16 1
 
< 0.1%
15 4
 
0.1%
14 11
 
0.4%
13 11
 
0.4%
12 34
1.1%
11 38
1.2%
10 25
0.8%

party_level3_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7874633
Minimum0
Maximum18
Zeros1667
Zeros (%)54.4%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:47.265847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile7
Maximum18
Range18
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5420461
Coefficient of variation (CV)1.4221529
Kurtosis3.5942882
Mean1.7874633
Median Absolute Deviation (MAD)0
Skewness1.7667428
Sum5475
Variance6.4619982
MonotonicityNot monotonic
2024-04-07T10:54:47.352649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1667
54.4%
3 527
 
17.2%
2 320
 
10.4%
6 135
 
4.4%
5 116
 
3.8%
4 68
 
2.2%
1 63
 
2.1%
8 48
 
1.6%
9 37
 
1.2%
7 29
 
0.9%
Other values (6) 53
 
1.7%
ValueCountFrequency (%)
0 1667
54.4%
1 63
 
2.1%
2 320
 
10.4%
3 527
 
17.2%
4 68
 
2.2%
5 116
 
3.8%
6 135
 
4.4%
7 29
 
0.9%
8 48
 
1.6%
9 37
 
1.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 1
 
< 0.1%
14 7
 
0.2%
12 10
 
0.3%
11 17
 
0.6%
10 17
 
0.6%
9 37
 
1.2%
8 48
 
1.6%
7 29
 
0.9%
6 135
4.4%

party_level4_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.85014691
Minimum0
Maximum15
Zeros2201
Zeros (%)71.9%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:47.439861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7339558
Coefficient of variation (CV)2.0395955
Kurtosis9.9694748
Mean0.85014691
Median Absolute Deviation (MAD)0
Skewness2.7726909
Sum2604
Variance3.0066027
MonotonicityNot monotonic
2024-04-07T10:54:47.521731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 2201
71.9%
3 304
 
9.9%
1 195
 
6.4%
2 176
 
5.7%
6 70
 
2.3%
4 39
 
1.3%
5 35
 
1.1%
9 15
 
0.5%
8 12
 
0.4%
7 6
 
0.2%
Other values (4) 10
 
0.3%
ValueCountFrequency (%)
0 2201
71.9%
1 195
 
6.4%
2 176
 
5.7%
3 304
 
9.9%
4 39
 
1.3%
5 35
 
1.1%
6 70
 
2.3%
7 6
 
0.2%
8 12
 
0.4%
9 15
 
0.5%
ValueCountFrequency (%)
15 2
 
0.1%
12 4
 
0.1%
11 3
 
0.1%
10 1
 
< 0.1%
9 15
 
0.5%
8 12
 
0.4%
7 6
 
0.2%
6 70
2.3%
5 35
1.1%
4 39
1.3%

party_level5_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47828926
Minimum0
Maximum15
Zeros2505
Zeros (%)81.8%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:47.599989image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.249991
Coefficient of variation (CV)2.6134624
Kurtosis20.581232
Mean0.47828926
Median Absolute Deviation (MAD)0
Skewness3.8061184
Sum1465
Variance1.5624775
MonotonicityNot monotonic
2024-04-07T10:54:47.684490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 2505
81.8%
2 240
 
7.8%
1 111
 
3.6%
3 105
 
3.4%
4 45
 
1.5%
5 20
 
0.7%
6 14
 
0.5%
7 9
 
0.3%
8 8
 
0.3%
11 2
 
0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
0 2505
81.8%
1 111
 
3.6%
2 240
 
7.8%
3 105
 
3.4%
4 45
 
1.5%
5 20
 
0.7%
6 14
 
0.5%
7 9
 
0.3%
8 8
 
0.3%
9 1
 
< 0.1%
ValueCountFrequency (%)
15 1
 
< 0.1%
12 1
 
< 0.1%
11 2
 
0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 8
 
0.3%
7 9
 
0.3%
6 14
 
0.5%
5 20
0.7%
4 45
1.5%

party_level6_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13940581
Minimum0
Maximum6
Zeros2768
Zeros (%)90.4%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:47.763928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.49009581
Coefficient of variation (CV)3.5156053
Kurtosis27.346495
Mean0.13940581
Median Absolute Deviation (MAD)0
Skewness4.6058701
Sum427
Variance0.2401939
MonotonicityNot monotonic
2024-04-07T10:54:47.839986image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 2768
90.4%
1 199
 
6.5%
2 71
 
2.3%
3 18
 
0.6%
4 4
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 2768
90.4%
1 199
 
6.5%
2 71
 
2.3%
3 18
 
0.6%
4 4
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 2
 
0.1%
4 4
 
0.1%
3 18
 
0.6%
2 71
 
2.3%
1 199
 
6.5%
0 2768
90.4%

party_level7_spellslots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.095004897
Minimum0
Maximum6
Zeros2867
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:47.913256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.42124879
Coefficient of variation (CV)4.4339693
Kurtosis49.49402
Mean0.095004897
Median Absolute Deviation (MAD)0
Skewness6.0921763
Sum291
Variance0.17745055
MonotonicityNot monotonic
2024-04-07T10:54:47.988014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 2867
93.6%
1 129
 
4.2%
2 50
 
1.6%
3 10
 
0.3%
4 5
 
0.2%
6 2
 
0.1%
ValueCountFrequency (%)
0 2867
93.6%
1 129
 
4.2%
2 50
 
1.6%
3 10
 
0.3%
4 5
 
0.2%
6 2
 
0.1%
ValueCountFrequency (%)
6 2
 
0.1%
4 5
 
0.2%
3 10
 
0.3%
2 50
 
1.6%
1 129
 
4.2%
0 2867
93.6%

party_level8_spellslots
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2911 
1
 
130
2
 
15
3
 
5
4
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2911
95.0%
1 130
 
4.2%
2 15
 
0.5%
3 5
 
0.2%
4 2
 
0.1%

Length

2024-04-07T10:54:48.076467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:48.156350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2911
95.0%
1 130
 
4.2%
2 15
 
0.5%
3 5
 
0.2%
4 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 2911
95.0%
1 130
 
4.2%
2 15
 
0.5%
3 5
 
0.2%
4 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2911
95.0%
1 130
 
4.2%
2 15
 
0.5%
3 5
 
0.2%
4 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2911
95.0%
1 130
 
4.2%
2 15
 
0.5%
3 5
 
0.2%
4 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2911
95.0%
1 130
 
4.2%
2 15
 
0.5%
3 5
 
0.2%
4 2
 
0.1%

party_level9_spellslots
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2976 
1
 
77
2
 
7
3
 
2
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2976
97.2%
1 77
 
2.5%
2 7
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Length

2024-04-07T10:54:48.240665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:48.315876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2976
97.2%
1 77
 
2.5%
2 7
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 2976
97.2%
1 77
 
2.5%
2 7
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2976
97.2%
1 77
 
2.5%
2 7
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2976
97.2%
1 77
 
2.5%
2 7
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2976
97.2%
1 77
 
2.5%
2 7
 
0.2%
3 2
 
0.1%
4 1
 
< 0.1%

party_total_ac
Real number (ℝ)

HIGH CORRELATION 

Distinct130
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.955599
Minimum9
Maximum175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:48.407677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile14
Q118
median33
Q364
95-th percentile96
Maximum175
Range166
Interquartile range (IQR)46

Descriptive statistics

Standard deviation28.889449
Coefficient of variation (CV)0.68857195
Kurtosis0.048585989
Mean41.955599
Median Absolute Deviation (MAD)17
Skewness0.9440511
Sum128510
Variance834.60025
MonotonicityNot monotonic
2024-04-07T10:54:48.516021image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 224
 
7.3%
17 188
 
6.1%
14 141
 
4.6%
16 138
 
4.5%
15 130
 
4.2%
19 126
 
4.1%
20 86
 
2.8%
33 69
 
2.3%
34 59
 
1.9%
13 55
 
1.8%
Other values (120) 1847
60.3%
ValueCountFrequency (%)
9 2
 
0.1%
10 27
 
0.9%
11 18
 
0.6%
12 46
 
1.5%
13 55
 
1.8%
14 141
4.6%
15 130
4.2%
16 138
4.5%
17 188
6.1%
18 224
7.3%
ValueCountFrequency (%)
175 1
 
< 0.1%
151 1
 
< 0.1%
147 1
 
< 0.1%
142 1
 
< 0.1%
140 3
0.1%
139 1
 
< 0.1%
138 1
 
< 0.1%
137 1
 
< 0.1%
136 1
 
< 0.1%
135 4
0.1%

party_total_precombat_hp
Real number (ℝ)

HIGH CORRELATION 

Distinct509
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean151.79758
Minimum8
Maximum3187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:48.625454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile27
Q155
median106
Q3200
95-th percentile427
Maximum3187
Range3179
Interquartile range (IQR)145

Descriptive statistics

Standard deviation149.08922
Coefficient of variation (CV)0.98215808
Kurtosis60.414399
Mean151.79758
Median Absolute Deviation (MAD)61
Skewness4.5033855
Sum464956
Variance22227.597
MonotonicityNot monotonic
2024-04-07T10:54:48.728916image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 43
 
1.4%
49 43
 
1.4%
27 38
 
1.2%
43 37
 
1.2%
24 36
 
1.2%
67 36
 
1.2%
31 36
 
1.2%
44 36
 
1.2%
51 33
 
1.1%
66 32
 
1.0%
Other values (499) 2693
87.9%
ValueCountFrequency (%)
8 1
 
< 0.1%
9 2
 
0.1%
10 4
 
0.1%
11 1
 
< 0.1%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 3
 
0.1%
17 12
0.4%
18 8
0.3%
ValueCountFrequency (%)
3187 1
< 0.1%
1096 1
< 0.1%
1053 1
< 0.1%
1036 1
< 0.1%
1013 1
< 0.1%
898 1
< 0.1%
889 1
< 0.1%
881 1
< 0.1%
874 1
< 0.1%
870 1
< 0.1%

party_total_postcombat_hp
Real number (ℝ)

HIGH CORRELATION 

Distinct346
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.36696
Minimum1
Maximum663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:48.845217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q123
median46
Q396
95-th percentile227.9
Maximum663
Range662
Interquartile range (IQR)73

Descriptive statistics

Standard deviation78.977366
Coefficient of variation (CV)1.0764705
Kurtosis8.5119807
Mean73.36696
Median Absolute Deviation (MAD)29
Skewness2.4923715
Sum224723
Variance6237.4244
MonotonicityNot monotonic
2024-04-07T10:54:48.969111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 57
 
1.9%
23 50
 
1.6%
10 48
 
1.6%
18 45
 
1.5%
24 44
 
1.4%
11 43
 
1.4%
21 40
 
1.3%
30 38
 
1.2%
13 38
 
1.2%
35 38
 
1.2%
Other values (336) 2622
85.6%
ValueCountFrequency (%)
1 30
1.0%
2 13
 
0.4%
3 19
 
0.6%
4 37
1.2%
5 20
0.7%
6 25
0.8%
7 35
1.1%
8 32
1.0%
9 33
1.1%
10 48
1.6%
ValueCountFrequency (%)
663 1
< 0.1%
632 1
< 0.1%
620 1
< 0.1%
551 1
< 0.1%
545 1
< 0.1%
542 1
< 0.1%
515 1
< 0.1%
505 1
< 0.1%
495 1
< 0.1%
494 1
< 0.1%

party_total_hpratio
Real number (ℝ)

Distinct70
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.47279465
Minimum0.01
Maximum0.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:49.078580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.13
Q10.36
median0.51
Q30.62
95-th percentile0.69
Maximum0.7
Range0.69
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.17753508
Coefficient of variation (CV)0.37550147
Kurtosis-0.41245871
Mean0.47279465
Median Absolute Deviation (MAD)0.12
Skewness-0.7193825
Sum1448.17
Variance0.031518706
MonotonicityIncreasing
2024-04-07T10:54:49.193201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.68 125
 
4.1%
0.67 103
 
3.4%
0.63 103
 
3.4%
0.69 95
 
3.1%
0.7 93
 
3.0%
0.65 90
 
2.9%
0.64 80
 
2.6%
0.61 78
 
2.5%
0.55 77
 
2.5%
0.59 76
 
2.5%
Other values (60) 2143
70.0%
ValueCountFrequency (%)
0.01 12
0.4%
0.02 10
0.3%
0.03 16
0.5%
0.04 15
0.5%
0.05 9
0.3%
0.06 11
0.4%
0.07 13
0.4%
0.08 11
0.4%
0.09 16
0.5%
0.1 10
0.3%
ValueCountFrequency (%)
0.7 93
3.0%
0.69 95
3.1%
0.68 125
4.1%
0.67 103
3.4%
0.66 67
2.2%
0.65 90
2.9%
0.64 80
2.6%
0.63 103
3.4%
0.62 69
2.3%
0.61 78
2.5%

party_total_prof_bonus
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8481881
Minimum2
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:49.294742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q13
median6
Q312
95-th percentile19
Maximum42
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.8419866
Coefficient of variation (CV)0.74437393
Kurtosis1.8825191
Mean7.8481881
Median Absolute Deviation (MAD)3
Skewness1.3593276
Sum24039
Variance34.128807
MonotonicityNot monotonic
2024-04-07T10:54:49.495318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
3 532
17.4%
2 343
11.2%
4 339
11.1%
6 295
9.6%
5 219
 
7.1%
12 184
 
6.0%
8 160
 
5.2%
15 149
 
4.9%
10 131
 
4.3%
9 117
 
3.8%
Other values (23) 594
19.4%
ValueCountFrequency (%)
2 343
11.2%
3 532
17.4%
4 339
11.1%
5 219
7.1%
6 295
9.6%
7 103
 
3.4%
8 160
 
5.2%
9 117
 
3.8%
10 131
 
4.3%
11 56
 
1.8%
ValueCountFrequency (%)
42 1
 
< 0.1%
36 3
 
0.1%
33 1
 
< 0.1%
31 3
 
0.1%
30 11
0.4%
29 5
0.2%
28 4
 
0.1%
27 3
 
0.1%
26 1
 
< 0.1%
25 12
0.4%

party_total_strength
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.174012
Minimum3
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:49.597782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q113
median23
Q348
95-th percentile77
Maximum152
Range149
Interquartile range (IQR)35

Descriptive statistics

Standard deviation23.118022
Coefficient of variation (CV)0.71853089
Kurtosis0.42784548
Mean32.174012
Median Absolute Deviation (MAD)13
Skewness1.0316236
Sum98549
Variance534.44293
MonotonicityNot monotonic
2024-04-07T10:54:49.708547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 215
 
7.0%
10 185
 
6.0%
18 150
 
4.9%
16 149
 
4.9%
20 125
 
4.1%
12 92
 
3.0%
9 92
 
3.0%
19 91
 
3.0%
13 90
 
2.9%
11 75
 
2.4%
Other values (103) 1799
58.7%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 4
 
0.1%
7 13
 
0.4%
8 215
7.0%
9 92
3.0%
10 185
6.0%
11 75
 
2.4%
12 92
3.0%
ValueCountFrequency (%)
152 1
 
< 0.1%
134 1
 
< 0.1%
126 1
 
< 0.1%
123 1
 
< 0.1%
114 3
0.1%
112 1
 
< 0.1%
110 2
0.1%
109 2
0.1%
107 1
 
< 0.1%
106 1
 
< 0.1%

party_total_dexterity
Real number (ℝ)

HIGH CORRELATION 

Distinct125
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.560235
Minimum5
Maximum157
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:49.817446image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile11
Q116
median29
Q357
95-th percentile86
Maximum157
Range152
Interquartile range (IQR)41

Descriptive statistics

Standard deviation26.089372
Coefficient of variation (CV)0.69460087
Kurtosis0.083795504
Mean37.560235
Median Absolute Deviation (MAD)15
Skewness0.94637152
Sum115047
Variance680.65534
MonotonicityNot monotonic
2024-04-07T10:54:49.931329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 233
 
7.6%
16 203
 
6.6%
18 194
 
6.3%
20 153
 
5.0%
13 95
 
3.1%
17 89
 
2.9%
10 85
 
2.8%
12 71
 
2.3%
30 58
 
1.9%
32 54
 
1.8%
Other values (115) 1828
59.7%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 14
 
0.5%
7 1
 
< 0.1%
8 34
 
1.1%
9 17
 
0.6%
10 85
 
2.8%
11 45
 
1.5%
12 71
 
2.3%
13 95
3.1%
14 233
7.6%
ValueCountFrequency (%)
157 1
< 0.1%
144 1
< 0.1%
137 1
< 0.1%
136 1
< 0.1%
133 1
< 0.1%
132 1
< 0.1%
131 1
< 0.1%
127 1
< 0.1%
126 1
< 0.1%
125 1
< 0.1%

party_total_constitution
Real number (ℝ)

HIGH CORRELATION 

Distinct118
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.089128
Minimum7
Maximum154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:50.045997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q116
median30
Q358
95-th percentile87
Maximum154
Range147
Interquartile range (IQR)42

Descriptive statistics

Standard deviation26.351631
Coefficient of variation (CV)0.69184127
Kurtosis0.089663481
Mean38.089128
Median Absolute Deviation (MAD)16
Skewness0.96546263
Sum116667
Variance694.40845
MonotonicityNot monotonic
2024-04-07T10:54:50.158411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14 298
 
9.7%
16 268
 
8.7%
18 177
 
5.8%
15 114
 
3.7%
12 104
 
3.4%
20 96
 
3.1%
13 89
 
2.9%
30 81
 
2.6%
17 78
 
2.5%
28 71
 
2.3%
Other values (108) 1687
55.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
9 1
 
< 0.1%
10 51
 
1.7%
11 10
 
0.3%
12 104
 
3.4%
13 89
 
2.9%
14 298
9.7%
15 114
 
3.7%
16 268
8.7%
17 78
 
2.5%
ValueCountFrequency (%)
154 1
 
< 0.1%
152 1
 
< 0.1%
136 1
 
< 0.1%
135 1
 
< 0.1%
131 1
 
< 0.1%
127 1
 
< 0.1%
126 2
0.1%
124 3
0.1%
122 3
0.1%
121 1
 
< 0.1%

party_total_intelligence
Real number (ℝ)

HIGH CORRELATION 

Distinct107
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.588965
Minimum5
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:50.272147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q111.5
median23
Q346
95-th percentile73
Maximum126
Range121
Interquartile range (IQR)34.5

Descriptive statistics

Standard deviation22.326635
Coefficient of variation (CV)0.72989179
Kurtosis0.29285736
Mean30.588965
Median Absolute Deviation (MAD)13
Skewness1.0076834
Sum93694
Variance498.47861
MonotonicityNot monotonic
2024-04-07T10:54:50.390031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 284
 
9.3%
10 234
 
7.6%
14 143
 
4.7%
11 134
 
4.4%
12 132
 
4.3%
20 119
 
3.9%
9 108
 
3.5%
18 89
 
2.9%
16 64
 
2.1%
24 61
 
2.0%
Other values (97) 1695
55.3%
ValueCountFrequency (%)
5 1
 
< 0.1%
7 5
 
0.2%
8 284
9.3%
9 108
 
3.5%
10 234
7.6%
11 134
4.4%
12 132
4.3%
13 50
 
1.6%
14 143
4.7%
15 18
 
0.6%
ValueCountFrequency (%)
126 1
< 0.1%
120 1
< 0.1%
117 1
< 0.1%
115 1
< 0.1%
113 1
< 0.1%
111 1
< 0.1%
110 1
< 0.1%
108 1
< 0.1%
105 1
< 0.1%
104 2
0.1%

party_total_wisdom
Real number (ℝ)

HIGH CORRELATION 

Distinct114
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.116552
Minimum4
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:50.493696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile10
Q114
median26
Q352
95-th percentile78
Maximum148
Range144
Interquartile range (IQR)38

Descriptive statistics

Standard deviation23.899675
Coefficient of variation (CV)0.70053018
Kurtosis0.20758335
Mean34.116552
Median Absolute Deviation (MAD)14
Skewness0.97304414
Sum104499
Variance571.19445
MonotonicityNot monotonic
2024-04-07T10:54:50.600095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 199
 
6.5%
14 175
 
5.7%
16 174
 
5.7%
13 174
 
5.7%
12 170
 
5.6%
11 84
 
2.7%
15 83
 
2.7%
20 82
 
2.7%
18 81
 
2.6%
26 67
 
2.2%
Other values (104) 1774
57.9%
ValueCountFrequency (%)
4 1
 
< 0.1%
7 1
 
< 0.1%
8 42
 
1.4%
9 16
 
0.5%
10 199
6.5%
11 84
2.7%
12 170
5.6%
13 174
5.7%
14 175
5.7%
15 83
2.7%
ValueCountFrequency (%)
148 1
< 0.1%
132 1
< 0.1%
129 1
< 0.1%
128 1
< 0.1%
126 1
< 0.1%
124 1
< 0.1%
119 1
< 0.1%
112 1
< 0.1%
111 2
0.1%
110 1
< 0.1%

party_total_charisma
Real number (ℝ)

HIGH CORRELATION 

Distinct113
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.349657
Minimum5
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:50.703942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile8
Q114
median24
Q350
95-th percentile80
Maximum152
Range147
Interquartile range (IQR)36

Descriptive statistics

Standard deviation24.139241
Coefficient of variation (CV)0.72382278
Kurtosis0.43918061
Mean33.349657
Median Absolute Deviation (MAD)13
Skewness1.039293
Sum102150
Variance582.70298
MonotonicityNot monotonic
2024-04-07T10:54:50.815294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 190
 
6.2%
8 182
 
5.9%
10 176
 
5.7%
14 138
 
4.5%
12 135
 
4.4%
18 134
 
4.4%
11 109
 
3.6%
16 99
 
3.2%
13 81
 
2.6%
22 57
 
1.9%
Other values (103) 1762
57.5%
ValueCountFrequency (%)
5 1
 
< 0.1%
6 8
 
0.3%
7 8
 
0.3%
8 182
5.9%
9 43
 
1.4%
10 176
5.7%
11 109
3.6%
12 135
4.4%
13 81
2.6%
14 138
4.5%
ValueCountFrequency (%)
152 1
 
< 0.1%
143 1
 
< 0.1%
136 1
 
< 0.1%
128 2
0.1%
119 1
 
< 0.1%
118 1
 
< 0.1%
116 1
 
< 0.1%
115 2
0.1%
113 2
0.1%
110 3
0.1%

player_monster_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.035445
Minimum0.014925373
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:50.951793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.014925373
5-th percentile0.14285714
Q10.44444444
median1
Q31
95-th percentile3
Maximum7
Range6.9850746
Interquartile range (IQR)0.55555556

Descriptive statistics

Standard deviation0.93991697
Coefficient of variation (CV)0.90774206
Kurtosis7.6686963
Mean1.035445
Median Absolute Deviation (MAD)0.5
Skewness2.4426192
Sum3171.568
Variance0.8834439
MonotonicityNot monotonic
2024-04-07T10:54:51.076256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1039
33.9%
0.5 281
 
9.2%
2 241
 
7.9%
0.3333333333 146
 
4.8%
0.6666666667 92
 
3.0%
0.25 84
 
2.7%
3 76
 
2.5%
0.2 62
 
2.0%
1.5 61
 
2.0%
0.4 50
 
1.6%
Other values (112) 931
30.4%
ValueCountFrequency (%)
0.014925373 1
 
< 0.1%
0.015463918 1
 
< 0.1%
0.018867925 1
 
< 0.1%
0.034482759 2
 
0.1%
0.04 9
0.3%
0.043478261 1
 
< 0.1%
0.045454545 2
 
0.1%
0.048780488 1
 
< 0.1%
0.049180328 1
 
< 0.1%
0.05 3
 
0.1%
ValueCountFrequency (%)
7 2
 
0.1%
6 10
 
0.3%
5 46
 
1.5%
4.5 1
 
< 0.1%
4 43
 
1.4%
3.5 3
 
0.1%
3 76
 
2.5%
2.5 41
 
1.3%
2.333333333 2
 
0.1%
2 241
7.9%

monster_player_ratio
Real number (ℝ)

HIGH CORRELATION 

Distinct122
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2262176
Minimum0.14285714
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:51.183879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.14285714
5-th percentile0.33333333
Q11
median1
Q32.25
95-th percentile7
Maximum67
Range66.857143
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation3.4720183
Coefficient of variation (CV)1.5596042
Kurtosis99.132143
Mean2.2262176
Median Absolute Deviation (MAD)0.5
Skewness7.5414085
Sum6818.9044
Variance12.054911
MonotonicityNot monotonic
2024-04-07T10:54:51.298069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1039
33.9%
2 281
 
9.2%
0.5 241
 
7.9%
3 146
 
4.8%
1.5 92
 
3.0%
4 84
 
2.7%
0.3333333333 76
 
2.5%
5 62
 
2.0%
0.6666666667 61
 
2.0%
2.5 50
 
1.6%
Other values (112) 931
30.4%
ValueCountFrequency (%)
0.1428571429 2
 
0.1%
0.1666666667 10
 
0.3%
0.2 46
 
1.5%
0.2222222222 1
 
< 0.1%
0.25 43
 
1.4%
0.2857142857 3
 
0.1%
0.3333333333 76
 
2.5%
0.4 41
 
1.3%
0.4285714286 2
 
0.1%
0.5 241
7.9%
ValueCountFrequency (%)
67 1
 
< 0.1%
64.66666667 1
 
< 0.1%
53 1
 
< 0.1%
29 2
 
0.1%
25 9
0.3%
23 1
 
< 0.1%
22 2
 
0.1%
20.5 1
 
< 0.1%
20.33333333 1
 
< 0.1%
20 3
 
0.1%

Blood Hunter
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2838 
1
 
225

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2838
92.7%
1 225
 
7.3%

Length

2024-04-07T10:54:51.400129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:51.471177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2838
92.7%
1 225
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 2838
92.7%
1 225
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2838
92.7%
1 225
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2838
92.7%
1 225
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2838
92.7%
1 225
 
7.3%

Ranger
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2574 
1
489 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2574
84.0%
1 489
 
16.0%

Length

2024-04-07T10:54:51.547814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:51.619061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2574
84.0%
1 489
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 2574
84.0%
1 489
 
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2574
84.0%
1 489
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2574
84.0%
1 489
 
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2574
84.0%
1 489
 
16.0%

Bard
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2592 
1
471 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2592
84.6%
1 471
 
15.4%

Length

2024-04-07T10:54:51.701329image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:51.896679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2592
84.6%
1 471
 
15.4%

Most occurring characters

ValueCountFrequency (%)
0 2592
84.6%
1 471
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2592
84.6%
1 471
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2592
84.6%
1 471
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2592
84.6%
1 471
 
15.4%

Rogue
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2156 
1
907 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2156
70.4%
1 907
29.6%

Length

2024-04-07T10:54:51.981520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:52.053727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2156
70.4%
1 907
29.6%

Most occurring characters

ValueCountFrequency (%)
0 2156
70.4%
1 907
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2156
70.4%
1 907
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2156
70.4%
1 907
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2156
70.4%
1 907
29.6%

Warlock
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2143 
1
920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2143
70.0%
1 920
30.0%

Length

2024-04-07T10:54:52.131758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:52.204279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2143
70.0%
1 920
30.0%

Most occurring characters

ValueCountFrequency (%)
0 2143
70.0%
1 920
30.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2143
70.0%
1 920
30.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2143
70.0%
1 920
30.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2143
70.0%
1 920
30.0%

Wizard
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2254 
1
809 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2254
73.6%
1 809
 
26.4%

Length

2024-04-07T10:54:52.280338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:52.363597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2254
73.6%
1 809
 
26.4%

Most occurring characters

ValueCountFrequency (%)
0 2254
73.6%
1 809
 
26.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2254
73.6%
1 809
 
26.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2254
73.6%
1 809
 
26.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2254
73.6%
1 809
 
26.4%

Druid
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2653 
1
410 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2653
86.6%
1 410
 
13.4%

Length

2024-04-07T10:54:52.443498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:52.514400image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2653
86.6%
1 410
 
13.4%

Most occurring characters

ValueCountFrequency (%)
0 2653
86.6%
1 410
 
13.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2653
86.6%
1 410
 
13.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2653
86.6%
1 410
 
13.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2653
86.6%
1 410
 
13.4%

Monk
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2519 
1
544 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2519
82.2%
1 544
 
17.8%

Length

2024-04-07T10:54:52.593654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:52.665233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2519
82.2%
1 544
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 2519
82.2%
1 544
 
17.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2519
82.2%
1 544
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2519
82.2%
1 544
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2519
82.2%
1 544
 
17.8%

Paladin
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2325 
1
738 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2325
75.9%
1 738
 
24.1%

Length

2024-04-07T10:54:52.743288image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:52.814605image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2325
75.9%
1 738
 
24.1%

Most occurring characters

ValueCountFrequency (%)
0 2325
75.9%
1 738
 
24.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2325
75.9%
1 738
 
24.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2325
75.9%
1 738
 
24.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2325
75.9%
1 738
 
24.1%

Fighter
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
1862 
1
1201 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1862
60.8%
1 1201
39.2%

Length

2024-04-07T10:54:52.894070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:52.967044image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1862
60.8%
1 1201
39.2%

Most occurring characters

ValueCountFrequency (%)
0 1862
60.8%
1 1201
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1862
60.8%
1 1201
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1862
60.8%
1 1201
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1862
60.8%
1 1201
39.2%

Barbarian
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2303 
1
760 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2303
75.2%
1 760
 
24.8%

Length

2024-04-07T10:54:53.044926image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:53.118988image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2303
75.2%
1 760
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0 2303
75.2%
1 760
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2303
75.2%
1 760
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2303
75.2%
1 760
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2303
75.2%
1 760
 
24.8%

Cleric
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2199 
1
864 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2199
71.8%
1 864
 
28.2%

Length

2024-04-07T10:54:53.205403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:53.292245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2199
71.8%
1 864
 
28.2%

Most occurring characters

ValueCountFrequency (%)
0 2199
71.8%
1 864
 
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2199
71.8%
1 864
 
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2199
71.8%
1 864
 
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2199
71.8%
1 864
 
28.2%

Sorcerer
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
0
2412 
1
651 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3063
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 2412
78.7%
1 651
 
21.3%

Length

2024-04-07T10:54:53.381969image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-07T10:54:53.461128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2412
78.7%
1 651
 
21.3%

Most occurring characters

ValueCountFrequency (%)
0 2412
78.7%
1 651
 
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2412
78.7%
1 651
 
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2412
78.7%
1 651
 
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2412
78.7%
1 651
 
21.3%

weighted_monster_level
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct448
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.027608
Minimum0
Maximum1573
Zeros69
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:53.551159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q16
median12
Q333.4375
95-th percentile141
Maximum1573
Range1573
Interquartile range (IQR)27.4375

Descriptive statistics

Standard deviation82.62665
Coefficient of variation (CV)2.2934259
Kurtosis101.7873
Mean36.027608
Median Absolute Deviation (MAD)9.5
Skewness8.0413601
Sum110352.56
Variance6827.1633
MonotonicityNot monotonic
2024-04-07T10:54:53.665516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 371
 
12.1%
9 106
 
3.5%
2 103
 
3.4%
5 84
 
2.7%
0.25 84
 
2.7%
0 69
 
2.3%
7 67
 
2.2%
15 66
 
2.2%
3 65
 
2.1%
1 57
 
1.9%
Other values (438) 1991
65.0%
ValueCountFrequency (%)
0 69
2.3%
0.125 24
 
0.8%
0.25 84
2.7%
0.3125 1
 
< 0.1%
0.375 21
 
0.7%
0.5 34
1.1%
0.5625 2
 
0.1%
0.625 1
 
< 0.1%
0.75 28
 
0.9%
1 57
1.9%
ValueCountFrequency (%)
1573 1
< 0.1%
1440 1
< 0.1%
1262 1
< 0.1%
966 1
< 0.1%
824.5 1
< 0.1%
773.5 1
< 0.1%
724 1
< 0.1%
664.5 1
< 0.1%
638 1
< 0.1%
613 1
< 0.1%

weighted_spell_slots
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct646
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean234.84558
Minimum0
Maximum3959
Zeros542
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size24.1 KiB
2024-04-07T10:54:53.777332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median107
Q3292.5
95-th percentile944.7
Maximum3959
Range3959
Interquartile range (IQR)274.5

Descriptive statistics

Standard deviation349.67814
Coefficient of variation (CV)1.4889705
Kurtosis17.058668
Mean234.84558
Median Absolute Deviation (MAD)107
Skewness3.2683513
Sum719332
Variance122274.8
MonotonicityNot monotonic
2024-04-07T10:54:53.896542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 542
 
17.7%
18 150
 
4.9%
58 134
 
4.4%
75 96
 
3.1%
12 84
 
2.7%
139 79
 
2.6%
171 52
 
1.7%
6 49
 
1.6%
215 47
 
1.5%
64 44
 
1.4%
Other values (636) 1786
58.3%
ValueCountFrequency (%)
0 542
17.7%
6 49
 
1.6%
12 84
 
2.7%
17 9
 
0.3%
18 150
 
4.9%
24 38
 
1.2%
29 5
 
0.2%
30 22
 
0.7%
32 2
 
0.1%
34 42
 
1.4%
ValueCountFrequency (%)
3959 1
< 0.1%
3773 1
< 0.1%
3472 1
< 0.1%
2563 1
< 0.1%
2488 1
< 0.1%
2417 1
< 0.1%
2396 1
< 0.1%
2299 1
< 0.1%
2292 1
< 0.1%
2282 1
< 0.1%

Interactions

2024-04-07T10:54:36.613005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:39.377967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.625530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:43.767104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:46.077094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:48.231735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.318065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.447861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.657885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.192703image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.612278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:01.857641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:03.964185image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:05.954053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:08.033061image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:10.188723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:12.469513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.738166image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:16.930416image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:19.090513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:21.215908image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.610195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.733893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.832476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:30.021370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:32.199659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.330450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.776884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:39.473252image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.704732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:43.851309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:46.156801image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:48.313496image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.398623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.531942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.745080image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.286339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.702114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:01.936856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:04.040637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:06.033579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:08.123259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:10.267172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:12.572848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.821755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:17.013192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:19.171791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:21.388701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.691412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.813452image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.911869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:30.130268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:32.278573image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.411133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.853154image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:39.552502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.780796image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:43.932570image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:46.234221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:48.387680image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.476368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.618160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.825621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.476369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.781411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:02.011202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:04.114955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:06.106925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:08.202266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:10.342193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:12.688650image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.901350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:17.091508image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:19.251846image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:21.468233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.765390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.890099image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.987424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:30.225873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:32.353746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.522034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.931848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:39.633030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.859929image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:44.081050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:46.309944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:48.464643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.552866image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.699896image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.905717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.554415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.865391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:02.086880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:04.188997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:06.259898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:08.279477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:10.417492image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:12.766706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.982677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:17.169710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:19.332033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:21.545696image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.841158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.966083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:28.063612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:30.303736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:32.429730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.652304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:37.010931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:39.719265image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.939898image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:44.196555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:46.386961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:48.539925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.633844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.783225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.986689image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.630981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.959600image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:02.163934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:04.264900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:06.332924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:08.355352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:10.493275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:12.849390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:15.067458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:17.247147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:19.413053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:21.627217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.917992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:26.044331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:28.140674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:30.383170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:32.511072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.751092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:37.088493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:39.797646image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:42.016210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:44.282242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:46.460352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:48.615474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.709321image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.862597image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:55.062984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.706681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-07T10:54:02.241140image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:04.339542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-07T10:54:08.432784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:10.566024image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:12.927188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:15.142655image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:17.322433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-07T10:54:21.703309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.991636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:26.116786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:28.213031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:30.457431image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:32.602668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.895527image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:37.161269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:39.872257image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:42.090317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-07T10:53:50.784281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.940853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:55.139919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.780780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-07T10:54:02.310405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:04.411093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-07T10:54:08.503351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-04-07T10:54:09.737630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:11.755375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.181884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:16.452961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:18.629982image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:20.728092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:22.989779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.278807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.305709image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:29.540522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:31.751371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:33.878125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.132014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:38.413357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.143298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:43.330692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:45.681590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:47.836228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:49.865724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.032780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.266945image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:56.675202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.233786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:01.451162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:03.581579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:05.577073image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:07.647366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:09.815033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:11.829069image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.257975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:16.530483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:18.703994image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:20.804586image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.072116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.358403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.377297image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:29.617599image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:31.824276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:33.950487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.214300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:38.490537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.303674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:43.406391image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:45.766603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:47.912621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:49.941664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.111051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.346634image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:56.843671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.308228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:01.525576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:03.661034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:05.648101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:07.727856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:09.889976image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:11.904938image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.335328image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:16.606102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:18.777850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:20.888337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.242269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.434876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.452224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:29.693867image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:31.900135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.025464image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.291581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:38.566050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.381424image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:43.509163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:45.841760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:47.992762image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.013467image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.192411image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.422214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:56.932458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.382098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:01.607853image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:03.731502image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:05.721053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:07.801555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:09.962524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:11.990758image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.413852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:16.688957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:18.853214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:20.971087image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.349134image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.506332image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.607173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:29.771147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:31.971860image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.101125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.371125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:38.642768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.460511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:43.600142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:45.918180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:48.067922image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.088066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.272352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.498741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.020145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.456235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:01.683123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:03.812607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:05.792382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:07.876608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:10.036078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:12.093254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.566592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:16.765450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:18.928188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:21.049997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.438385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.579462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.680212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:29.861242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:32.046157image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.173577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.448848image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:38.721584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:41.543532image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:43.680910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:45.996184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:48.147732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:50.239950image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:52.369470image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:54.577488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:57.103490image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:53:59.533379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:01.761155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:03.887215image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:05.871226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:07.953949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:10.112489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:12.378159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:14.652409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:16.846946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:19.006606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:21.132924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:23.530423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:25.657483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:27.756489image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:29.941195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:32.122578image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:34.250697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-07T10:54:36.529162image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-04-07T10:54:54.019769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
BarbarianBardBlood HunterClericDruidFighterMonkPaladinRangerRogueSorcererWarlockWizardmonster_numbermonster_player_ratiomonster_total_levelparty_level1_spellslotsparty_level2_spellslotsparty_level3_spellslotsparty_level4_spellslotsparty_level5_spellslotsparty_level6_spellslotsparty_level7_spellslotsparty_level8_spellslotsparty_level9_spellslotsparty_sizeparty_total_acparty_total_charismaparty_total_constitutionparty_total_dexterityparty_total_hpratioparty_total_intelligenceparty_total_levelparty_total_postcombat_hpparty_total_precombat_hpparty_total_prof_bonusparty_total_strengthparty_total_wisdomplayer_monster_ratiostart_timeweighted_monster_levelweighted_spell_slots
Barbarian1.0000.0000.0170.0270.0520.1010.0470.0390.0000.0920.0180.0000.0510.2030.0050.1380.0730.014-0.005-0.018-0.037-0.061-0.0530.0410.0670.3040.2770.2460.3290.2930.0060.2600.2160.2280.2610.2660.3960.252-0.0050.0160.175-0.014
Bard0.0001.0000.0000.0180.0250.0000.0110.0000.0240.0500.0940.0790.0030.085-0.0770.0800.3070.2930.2480.1830.1090.0880.0830.1040.0530.2190.2030.2980.2120.2110.0270.1860.1980.1570.1780.2120.1690.1770.0770.0050.0860.284
Blood Hunter0.0170.0001.0000.0130.0000.0000.0170.0290.0350.0270.0000.0000.0000.0930.0020.1040.0270.0210.027-0.0040.0030.010-0.0330.0090.0000.1500.1560.1160.1300.172-0.0180.1670.1220.0980.1270.1430.1330.148-0.0020.0010.1100.002
Cleric0.0270.0180.0131.0000.1220.0090.0110.0270.0320.0170.1260.0000.1140.159-0.0490.1810.4300.4060.3440.2550.1440.1600.1360.1160.1220.3220.3660.2820.3210.2680.0810.3060.2830.2550.2620.3240.2970.4120.049-0.0230.1990.394
Druid0.0520.0250.0000.1221.0000.0000.0800.0190.0000.0260.0000.0000.0000.068-0.0690.0860.2610.2420.1870.1520.1290.1300.1280.1080.0460.2030.1980.1510.2060.1700.0350.1750.1620.1540.1620.1940.1760.2700.069-0.0120.0940.242
Fighter0.1010.0000.0000.0090.0001.0000.0630.0310.0430.1070.0530.0220.0090.182-0.0680.2480.1040.0830.0580.0220.0060.0240.0060.0000.0000.3620.3860.2780.3910.3620.0480.3220.3630.3470.3850.3820.4160.3170.068-0.0440.2590.032
Monk0.0470.0110.0170.0110.0800.0631.0000.0220.0090.0000.0280.0430.0000.082-0.1110.1480.0840.0690.0500.0270.0120.014-0.0030.0560.0270.2730.2710.2100.2310.3010.0140.2290.2370.1830.2070.2610.2150.3240.111-0.0310.1520.039
Paladin0.0390.0000.0290.0270.0190.0310.0221.0000.0000.0150.1420.2390.0000.151-0.0640.2350.3740.2740.1380.0700.0430.0070.0020.0460.0000.3170.3600.3970.3180.2260.0620.2440.2770.2870.2950.3080.3770.2560.064-0.0190.2400.236
Ranger0.0000.0240.0350.0320.0000.0430.0090.0001.0000.1090.0000.0000.0000.1340.0030.1130.2430.1470.0570.010-0.0060.0130.0250.0210.0150.2240.2270.1710.2050.2650.0050.2030.1740.1520.1710.2090.1960.275-0.003-0.0200.1320.117
Rogue0.0920.0500.0270.0170.0260.1070.0000.0150.1091.0000.0740.0000.0550.142-0.0870.1490.1360.0730.0310.0070.0070.0100.0170.0000.0000.3510.3140.3270.2950.4100.0350.3480.2790.2220.2350.3250.2690.3130.087-0.0010.1670.043
Sorcerer0.0180.0940.0000.1260.0000.0530.0280.1420.0000.0741.0000.2010.0000.156-0.0860.1870.4060.3660.3090.2040.1270.1250.0970.1060.0930.3570.3360.4280.3530.3140.0780.3260.3030.2700.2670.3440.3040.3110.086-0.0360.2060.349
Warlock0.0000.0790.0000.0000.0000.0220.0430.2390.0000.0000.2011.0000.0120.155-0.0600.1750.2760.2520.1850.1250.1380.0530.0450.0590.0400.3100.3150.4320.3100.2710.0240.2740.2970.2550.2790.3200.2700.2600.060-0.0360.1890.282
Wizard0.0510.0030.0000.1140.0000.0090.0000.0000.0000.0550.0000.0121.0000.144-0.0340.0760.3610.3290.2730.2360.1590.1580.1420.1010.1070.2650.1950.2160.2310.2670.0060.4190.2260.1320.1570.2500.1890.2240.0340.0200.1080.331
monster_number0.2030.0850.0930.1590.0680.1820.0820.1510.1340.1420.1560.1550.1441.0000.7400.4180.3690.2940.2220.1180.0610.0350.0200.0000.0000.4720.4720.4680.4880.458-0.0020.4820.3840.3310.3820.4500.4830.460-0.7400.0230.6240.258
monster_player_ratio0.005-0.0770.002-0.049-0.069-0.068-0.111-0.0640.003-0.087-0.086-0.060-0.0340.7401.0000.149-0.105-0.094-0.037-0.040-0.028-0.043-0.0340.0000.000-0.196-0.160-0.156-0.146-0.170-0.073-0.136-0.109-0.107-0.091-0.143-0.130-0.165-1.0000.0770.315-0.078
monster_total_level0.1380.0800.1040.1810.0860.2480.1480.2350.1130.1490.1870.1750.0760.4180.1491.0000.4190.4520.4660.3860.3290.2510.2310.1320.1350.4290.4950.4040.4720.401-0.0240.3940.6820.5600.6880.6140.4220.421-0.149-0.0810.9590.493
party_level1_spellslots0.0730.3070.0270.4300.2610.1040.0840.3740.2430.1360.4060.2760.3610.369-0.1050.4191.0000.8540.6220.4330.2690.2420.1890.1560.1970.7190.7060.7310.6950.6460.1020.7110.6450.5450.5830.7110.6460.7070.105-0.0680.4560.801
party_level2_spellslots0.0140.2930.0210.4060.2420.0830.0690.2740.1470.0730.3660.2520.3290.294-0.0940.4520.8541.0000.7370.5340.3540.3060.2400.1650.1290.5920.5900.6170.5900.5220.1010.5950.6520.5440.5850.6510.5210.5900.094-0.0690.4590.873
party_level3_spellslots-0.0050.2480.0270.3440.1870.0580.0500.1380.0570.0310.3090.1850.2730.222-0.0370.4660.6220.7371.0000.7340.5020.4240.3380.3360.3490.4040.4370.4390.4350.3690.0750.4270.6300.5110.5660.5750.3550.4230.037-0.0350.4410.863
party_level4_spellslots-0.0180.183-0.0040.2550.1520.0220.0270.0700.0100.0070.2040.1250.2360.118-0.0400.3860.4330.5340.7341.0000.7260.5910.4810.4780.3970.2470.2980.2730.2880.2330.0450.2730.5390.4280.4870.4480.2200.2770.040-0.0310.3400.748
party_level5_spellslots-0.0370.1090.0030.1440.1290.0060.0120.043-0.0060.0070.1270.1380.1590.061-0.0280.3290.2690.3540.5020.7261.0000.7190.5850.6250.6980.1400.2020.1750.1870.1360.0300.1670.4660.3730.4310.3730.1160.1690.0280.0020.2760.620
party_level6_spellslots-0.0610.0880.0100.1600.1300.0240.0140.0070.0130.0100.1250.0530.1580.035-0.0430.2510.2420.3060.4240.5910.7191.0000.7900.6710.6880.1220.1740.1320.1580.1230.0330.1560.3730.2980.3450.3010.0960.1550.043-0.0070.2090.493
party_level7_spellslots-0.0530.083-0.0330.1360.1280.006-0.0030.0020.0250.0170.0970.0450.1420.020-0.0340.2310.1890.2400.3380.4810.5850.7901.0000.6460.7070.0830.1390.0920.1240.0830.0100.1060.3120.2460.2940.2510.0670.1210.034-0.0100.1920.408
party_level8_spellslots0.0410.1040.0090.1160.1080.0000.0560.0460.0210.0000.1060.0590.1010.0000.0000.1320.1560.1650.3360.4780.6250.6710.6461.0000.6270.0620.1090.0790.1030.0690.0120.0870.2800.2230.2700.2210.0440.0960.035-0.0180.1730.358
party_level9_spellslots0.0670.0530.0000.1220.0460.0000.0270.0000.0150.0000.0930.0400.1070.0000.0000.1350.1970.1290.3490.3970.6980.6880.7070.6271.0000.0390.0840.0450.0710.0480.0030.0670.2170.1610.2050.1710.0180.0610.0020.0160.1410.277
party_size0.3040.2190.1500.3220.2030.3620.2730.3170.2240.3510.3570.3100.2650.472-0.1960.4290.7190.5920.4040.2470.1400.1220.0830.0620.0391.0000.9400.9290.9430.9390.0970.9300.7360.6450.7000.8870.9210.9390.196-0.0880.5010.515
party_total_ac0.2770.2030.1560.3660.1980.3860.2710.3600.2270.3140.3360.3150.1950.472-0.1600.4950.7060.5900.4370.2980.2020.1740.1390.1090.0840.9401.0000.8930.9410.9060.1090.8740.8000.7130.7830.9140.9170.9250.160-0.0870.5540.541
party_total_charisma0.2460.2980.1160.2820.1510.2780.2100.3970.1710.3270.4280.4320.2160.468-0.1560.4040.7310.6170.4390.2730.1750.1320.0920.0790.0450.9290.8931.0000.8940.8730.0940.8750.7170.6200.6720.8530.8680.8620.156-0.0850.4760.569
party_total_constitution0.3290.2120.1300.3210.2060.3910.2310.3180.2050.2950.3530.3100.2310.488-0.1460.4720.6950.5900.4350.2880.1870.1580.1240.1030.0710.9430.9410.8941.0000.8870.1030.8820.7700.7040.7730.8960.9160.9040.146-0.0810.5400.534
party_total_dexterity0.2930.2110.1720.2680.1700.3620.3010.2260.2650.4100.3140.2710.2670.458-0.1700.4010.6460.5220.3690.2330.1360.1230.0830.0690.0480.9390.9060.8730.8871.0000.0860.9170.7290.6210.6800.8650.8480.9170.170-0.0600.4720.451
party_total_hpratio0.0060.027-0.0180.0810.0350.0480.0140.0620.0050.0350.0780.0240.006-0.002-0.073-0.0240.1020.1010.0750.0450.0300.0330.0100.0120.0030.0970.1090.0940.1030.0861.0000.0810.0680.4800.0720.0850.0970.0940.0730.013-0.0160.081
party_total_intelligence0.2600.1860.1670.3060.1750.3220.2290.2440.2030.3480.3260.2740.4190.482-0.1360.3940.7110.5950.4270.2730.1670.1560.1060.0870.0670.9300.8740.8750.8820.9170.0811.0000.7130.5970.6520.8500.8480.8960.136-0.0660.4760.532
party_total_level0.2160.1980.1220.2830.1620.3630.2370.2770.1740.2790.3030.2970.2260.384-0.1090.6820.6450.6520.6300.5390.4660.3730.3120.2800.2170.7360.8000.7170.7700.7290.0680.7131.0000.8310.9580.9470.7160.7290.109-0.0570.6790.705
party_total_postcombat_hp0.2280.1570.0980.2550.1540.3470.1830.2870.1520.2220.2700.2550.1320.331-0.1070.5600.5450.5440.5110.4280.3730.2980.2460.2230.1610.6450.7130.6200.7040.6210.4800.5970.8311.0000.8680.8040.6520.6300.107-0.0430.5650.571
party_total_precombat_hp0.2610.1780.1270.2620.1620.3850.2070.2950.1710.2350.2670.2790.1570.382-0.0910.6880.5830.5850.5660.4870.4310.3450.2940.2700.2050.7000.7830.6720.7730.6800.0720.6520.9580.8681.0000.9100.7080.6890.091-0.0530.6840.632
party_total_prof_bonus0.2660.2120.1430.3240.1940.3820.2610.3080.2090.3250.3440.3200.2500.450-0.1430.6140.7110.6510.5750.4480.3730.3010.2510.2210.1710.8870.9140.8530.8960.8650.0850.8500.9470.8040.9101.0000.8470.8660.143-0.0680.6440.667
party_total_strength0.3960.1690.1330.2970.1760.4160.2150.3770.1960.2690.3040.2700.1890.483-0.1300.4220.6460.5210.3550.2200.1160.0960.0670.0440.0180.9210.9170.8680.9160.8480.0970.8480.7160.6520.7080.8471.0000.8680.130-0.0950.4950.442
party_total_wisdom0.2520.1770.1480.4120.2700.3170.3240.2560.2750.3130.3110.2600.2240.460-0.1650.4210.7070.5900.4230.2770.1690.1550.1210.0960.0610.9390.9250.8620.9040.9170.0940.8960.7290.6300.6890.8660.8681.0000.165-0.0890.4890.526
player_monster_ratio-0.0050.077-0.0020.0490.0690.0680.1110.064-0.0030.0870.0860.0600.034-0.740-1.000-0.1490.1050.0940.0370.0400.0280.0430.0340.0350.0020.1960.1600.1560.1460.1700.0730.1360.1090.1070.0910.1430.1300.1651.000-0.077-0.3150.078
start_time0.0160.0050.001-0.023-0.012-0.044-0.031-0.019-0.020-0.001-0.036-0.0360.0200.0230.077-0.081-0.068-0.069-0.035-0.0310.002-0.007-0.010-0.0180.016-0.088-0.087-0.085-0.081-0.0600.013-0.066-0.057-0.043-0.053-0.068-0.095-0.089-0.0771.000-0.069-0.054
weighted_monster_level0.1750.0860.1100.1990.0940.2590.1520.2400.1320.1670.2060.1890.1080.6240.3150.9590.4560.4590.4410.3400.2760.2090.1920.1730.1410.5010.5540.4760.5400.472-0.0160.4760.6790.5650.6840.6440.4950.489-0.315-0.0691.0000.479
weighted_spell_slots-0.0140.2840.0020.3940.2420.0320.0390.2360.1170.0430.3490.2820.3310.258-0.0780.4930.8010.8730.8630.7480.6200.4930.4080.3580.2770.5150.5410.5690.5340.4510.0810.5320.7050.5710.6320.6670.4420.5260.078-0.0540.4791.000

Missing values

2024-04-07T10:54:38.909413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-07T10:54:39.267886image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

combat_idstart_timeplayer_idsplayer_infomonsters_infoparty_sizetotal_slotstotal_max_slotsparty_classes_with_levelparty_total_class_compositionplayer_individual_hp_ratiosplayer_individual_acplayer_individual_prof_bonusplayer_individual_strengthplayer_individual_dexterityplayer_individual_constitutionplayer_individual_intelligenceplayer_individual_wisdomplayer_individual_charismamonster_typesmonster_numbermonster_total_levelparty_total_levelparty_level1_spellslotsparty_level2_spellslotsparty_level3_spellslotsparty_level4_spellslotsparty_level5_spellslotsparty_level6_spellslotsparty_level7_spellslotsparty_level8_spellslotsparty_level9_spellslotsparty_total_acparty_total_precombat_hpparty_total_postcombat_hpparty_total_hpratioparty_total_prof_bonusparty_total_strengthparty_total_dexterityparty_total_constitutionparty_total_intelligenceparty_total_wisdomparty_total_charismaplayer_monster_ratiomonster_player_ratioBlood HunterRangerBardRogueWarlockWizardDruidMonkPaladinFighterBarbarianClericSorcererweighted_monster_levelweighted_spell_slots
01663286431-4184e897-6036-4e34-825e-a4681bd884d91.663286e+09['171301054641998890', '227204640385900092', '131491822985054365'][{'hp_ratio': (1, 85), 'class': [('Wizard', 10)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 13, 'stats': {'prof_bonus': 4, 'strength': 12, 'dexterity': 14, 'constitution': 19, 'intelligence': 20, 'wisdom': 13, 'charisma': 13}}, {'hp_ratio': None, 'class': [('Wizard', 11)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}, 'ac': 13, 'stats': {'prof_bonus': 4, 'strength': 11, 'dexterity': 17, 'constitution': 19, 'intelligence': 20, 'wisdom': 12, 'charisma': 11}}, {'hp_ratio': None, 'class': [('Bard', 5)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 14, 'stats': {'prof_bonus': 3, 'strength': 11, 'dexterity': 16, 'constitution': 16, 'intelligence': 11, 'wisdom': 12, 'charisma': 20}}, {'hp_ratio': None, 'class': [('Monk', 3)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 2, 'strength': 12, 'dexterity': 18, 'constitution': 14, 'intelligence': 12, 'wisdom': 18, 'charisma': 9}}][{'monster_id': '1a9bc800-94fd-49f7-a272-f697bc55afd0', 'monster_code': 'beholdy', 'monster_name': 'Aberrant Spirit', 'level': 0.125}]3{'1': 12, '2': 9, '3': 8, '4': 6, '5': 4, '6': 1, '7': 0, '8': 0, '9': 0}{'1': 12, '2': 9, '3': 8, '4': 6, '5': 4, '6': 1, '7': 0, '8': 0, '9': 0}[('Wizard', 10), ('Wizard', 11), ('Bard', 5), ('Monk', 3)]['Wizard', 'Wizard', 'Bard', 'Monk'][(1, 85)][13, 13, 14, 18][4, 4, 3, 2][12, 11, 11, 12][14, 17, 16, 18][19, 19, 16, 14][20, 20, 11, 12][13, 12, 12, 18][13, 11, 20, 9]['Aberrant Spirit']10.125291298641000588510.01134665686355533.0000000.33333300100101000000.1251074
11664579509-d9e3965d-ae0a-4500-8cf8-9950b4de88cd1.664580e+09['153068692087943643', '254235062041722309', '266753248612443040', '252465523787953538', '433213981677296135', '250953387656881590', '216435879949543624'][{'hp_ratio': None, 'class': [('Barbarian', 5)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 3, 'strength': 19, 'dexterity': 16, 'constitution': 18, 'intelligence': 14, 'wisdom': 16, 'charisma': 13}}, {'hp_ratio': None, 'class': [('Druid', 4)], 'slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 2, 'strength': 12, 'dexterity': 16, 'constitution': 16, 'intelligence': 13, 'wisdom': 20, 'charisma': 14}}, {'hp_ratio': None, 'class': [('Fighter', 3)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 2, 'strength': 14, 'dexterity': 19, 'constitution': 16, 'intelligence': 16, 'wisdom': 16, 'charisma': 16}}, {'hp_ratio': None, 'class': [('Cleric', 6)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 10, 'dexterity': 15, 'constitution': 16, 'intelligence': 13, 'wisdom': 20, 'charisma': 19}}, {'hp_ratio': None, 'class': [('Blood Hunter', 2), ('Cleric', 1)], 'slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 2, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 16, 'stats': {'prof_bonus': 2, 'strength': 12, 'dexterity': 19, 'constitution': 16, 'intelligence': 13, 'wisdom': 18, 'charisma': 15}}, {'hp_ratio': None, 'class': [('Ranger', 6)], 'slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 20, 'stats': {'prof_bonus': 3, 'strength': 13, 'dexterity': 20, 'constitution': 20, 'intelligence': 14, 'wisdom': 16, 'charisma': 15}}, {'hp_ratio': (1, 98), 'class': [('Barbarian', 5), ('Fighter', 3)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 3, 'strength': 18, 'dexterity': 16, 'constitution': 20, 'intelligence': 13, 'wisdom': 13, 'charisma': 12}}][{'monster_id': '65e44831-1349-44de-8581-27792a3e074b', 'monster_code': 'BA3', 'monster_name': 'Basilisk', 'level': 3.0}, {'monster_id': '2c97d1f5-98ab-44cb-882a-fc231e12f65a', 'monster_code': 'BA2', 'monster_name': 'Basilisk', 'level': 3.0}, {'monster_id': 'b0ed09f3-fa4f-4fd0-8d8a-55041cebd30c', 'monster_code': 'BA1', 'monster_name': 'Basilisk', 'level': 3.0}, {'monster_id': '5d53b9dd-99de-47b9-8d9a-3077edb0051e', 'monster_code': 'THC1', 'monster_name': 'Two-Headed Cerberus', 'level': 2.0}, {'monster_id': '5f041113-8822-4d6a-9b5d-40e66167cfec', 'monster_code': 'GO1', 'monster_name': 'Gorgon', 'level': 5.0}, {'monster_id': '4c80ded9-f236-4fcd-8eac-fb9d91567eb4', 'monster_code': 'SH2', 'monster_name': 'Setessan Hoplite', 'level': 4.0}, {'monster_id': 'bf133f4b-d6b3-4a90-8bb5-24bc47ba6b6a', 'monster_code': 'SH1', 'monster_name': 'Setessan Hoplite', 'level': 4.0}, {'monster_id': 'f68dc49b-aae3-4c25-8be5-64b396277021', 'monster_code': 'SH3', 'monster_name': 'Setessan Hoplite', 'level': 4.0}, {'monster_id': 'f8b07182-a6f3-4ce3-9e7d-6cc041899d9b', 'monster_code': 'MH1', 'monster_name': 'Meletian Hoplite', 'level': 3.0}, {'monster_id': 'e510ce1d-1ac6-4c13-b483-2cd64f16b1fa', 'monster_code': 'DS1', 'monster_name': 'Duergar Spy', 'level': 2.0}, {'monster_id': 'cf5c5615-b703-424d-9057-03339ad3925c', 'monster_code': 'LC1', 'monster_name': 'Living Cloudkill', 'level': 7.0}, {'monster_id': 'c513e8d6-3611-4ac0-a9cc-f2c2a240cc83', 'monster_code': 'DK1', 'monster_name': 'Death Knight', 'level': 17.0}]7{'1': 14, '2': 7, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 14, '2': 8, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Barbarian', 5), ('Druid', 4), ('Fighter', 3), ('Cleric', 6), ('Blood Hunter', 2), ('Cleric', 1), ('Ranger', 6), ('Barbarian', 5), ('Fighter', 3)]['Barbarian', 'Druid', 'Fighter', 'Cleric', 'Blood Hunter', 'Cleric', 'Ranger', 'Barbarian', 'Fighter'][(1, 98)][17, 16, 16, 19, 16, 20, 19][3, 2, 2, 3, 2, 3, 3][19, 12, 14, 10, 12, 13, 18][16, 16, 19, 15, 19, 20, 16][18, 16, 16, 16, 16, 20, 20][14, 13, 16, 13, 13, 14, 13][16, 20, 16, 20, 18, 16, 13][13, 14, 16, 19, 15, 15, 12]['Basilisk', 'Basilisk', 'Basilisk', 'Two-Headed Cerberus', 'Gorgon', 'Setessan Hoplite', 'Setessan Hoplite', 'Setessan Hoplite', 'Meletian Hoplite', 'Duergar Spy', 'Living Cloudkill', 'Death Knight']1257.0003514730000001239810.011898121122961191040.5833331.7142861100001001110171.000299
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81667701720-bb0a9408-2978-443b-920a-15089cfd9a7b1.667702e+09['717420956987500614'][{'hp_ratio': (1, 130), 'class': [('Wizard', 4), ('Barbarian', 3), ('Cleric', 1), ('Druid', 6)], 'slots': {'1': 4, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 5, 'strength': 18, 'dexterity': 18, 'constitution': 18, 'intelligence': 20, 'wisdom': 19, 'charisma': 18}}][{'monster_id': '1467acc7-ecb8-4bda-ac1e-2351d9955280', 'monster_code': 'GW1', 'monster_name': 'Giant Wasp', 'level': 0.5}, {'monster_id': 'e0372b45-6083-4e74-93ab-52279593d040', 'monster_code': 'GW2', 'monster_name': 'Giant Wasp', 'level': 0.5}, {'monster_id': '0f07d8a4-dda6-4d9f-87e1-31ce3cf4d834', 'monster_code': 'SK1', 'monster_name': 'Spider King', 'level': 1.0}, {'monster_id': '11296390-54d0-4bae-bc89-af0ab7cbb0d3', 'monster_code': 'MA1', 'monster_name': 'Madcap', 'level': 3.0}, {'monster_id': 'ef055cd2-618d-4efe-9e03-d6849e5a0b8b', 'monster_code': 'HIS1', 'monster_name': 'Hobgoblin Iron Shadow', 'level': 2.0}]1{'1': 4, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}[('Wizard', 4), ('Barbarian', 3), ('Cleric', 1), ('Druid', 6)]['Wizard', 'Barbarian', 'Cleric', 'Druid'][(1, 130)][19][5][18][18][18][20][19][18]['Giant Wasp', 'Giant Wasp', 'Spider King', 'Madcap', 'Hobgoblin Iron Shadow']57.000144000000001913010.0151818182019180.2000005.000000000001100011014.00024
91667067972-e07fb1c0-a0e7-4651-af77-1a7c9f6829a51.667068e+09['717420956987500614'][{'hp_ratio': (1, 130), 'class': [('Wizard', 4), ('Barbarian', 3), ('Cleric', 1), ('Druid', 6)], 'slots': {'1': 4, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 5, 'strength': 18, 'dexterity': 18, 'constitution': 18, 'intelligence': 20, 'wisdom': 19, 'charisma': 18}}][{'monster_id': '5a2ebe68-5ba4-432a-8a9e-422fabf71a1c', 'monster_code': 'NA1', 'monster_name': 'Naiad', 'level': 2.0}, {'monster_id': '65db4d09-1aa0-41b3-98e5-5f418d456bb7', 'monster_code': 'EL1', 'monster_name': 'Elephant', 'level': 4.0}]1{'1': 4, '2': 1, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 3, '4': 3, '5': 2, '6': 1, '7': 0, '8': 0, '9': 0}[('Wizard', 4), ('Barbarian', 3), ('Cleric', 1), ('Druid', 6)]['Wizard', 'Barbarian', 'Cleric', 'Druid'][(1, 130)][19][5][18][18][18][20][19][18]['Naiad', 'Elephant']26.000144100000001913010.0151818182019180.5000002.00000000000110001109.00041
combat_idstart_timeplayer_idsplayer_infomonsters_infoparty_sizetotal_slotstotal_max_slotsparty_classes_with_levelparty_total_class_compositionplayer_individual_hp_ratiosplayer_individual_acplayer_individual_prof_bonusplayer_individual_strengthplayer_individual_dexterityplayer_individual_constitutionplayer_individual_intelligenceplayer_individual_wisdomplayer_individual_charismamonster_typesmonster_numbermonster_total_levelparty_total_levelparty_level1_spellslotsparty_level2_spellslotsparty_level3_spellslotsparty_level4_spellslotsparty_level5_spellslotsparty_level6_spellslotsparty_level7_spellslotsparty_level8_spellslotsparty_level9_spellslotsparty_total_acparty_total_precombat_hpparty_total_postcombat_hpparty_total_hpratioparty_total_prof_bonusparty_total_strengthparty_total_dexterityparty_total_constitutionparty_total_intelligenceparty_total_wisdomparty_total_charismaplayer_monster_ratiomonster_player_ratioBlood HunterRangerBardRogueWarlockWizardDruidMonkPaladinFighterBarbarianClericSorcererweighted_monster_levelweighted_spell_slots
30531661954608-2402b7e3-91f0-418f-9805-62a3b5270ca41.661955e+09['229128858766373740', '156982178525322073'][{'hp_ratio': (76, 76), 'class': [('Fighter', 8)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 20, 'stats': {'prof_bonus': 3, 'strength': 20, 'dexterity': 10, 'constitution': 16, 'intelligence': 8, 'wisdom': 14, 'charisma': 8}}, {'hp_ratio': (19, 59), 'class': [('Monk', 8)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 3, 'strength': 8, 'dexterity': 18, 'constitution': 14, 'intelligence': 10, 'wisdom': 16, 'charisma': 10}}][{'monster_id': '4b9c1f69-88df-4d2b-98ea-0592041a3d50', 'monster_code': 'OBM1', 'monster_name': 'Oriq Blood Mage', 'level': 9.0}]2{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Fighter', 8), ('Monk', 8)]['Fighter', 'Monk'][(76, 76), (19, 59)][20, 17][3, 3][20, 8][10, 18][16, 14][8, 10][14, 16][8, 10]['Oriq Blood Mage']19.01600000000037135950.762828301830182.0000000.50000000000001010009.00
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30551664534028-e0629083-8c86-4751-af51-f2805825ad8a1.664534e+09['199245625511395727'][{'hp_ratio': (23, 33), 'class': [('Druid', 4), ('Monk', 1)], 'slots': {'1': 2, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 21, 'stats': {'prof_bonus': 3, 'strength': 10, 'dexterity': 20, 'constitution': 13, 'intelligence': 8, 'wisdom': 20, 'charisma': 10}}][{'monster_id': '78ab9a1a-58c5-4faf-a2f9-a28346d5545a', 'monster_code': 'RP1', 'monster_name': 'Replica Pentadrone', 'level': 2.0}]1{'1': 2, '2': 2, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 3, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Druid', 4), ('Monk', 1)]['Druid', 'Monk'][(23, 33)][21][3][10][20][13][8][20][10]['Replica Pentadrone']12.052200000002133230.73102013820101.0000001.00000000000011000002.046
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30591660313660-01e1c0e9-1eea-42b3-8402-a9472f37f46c1.660314e+09['132018690291336574'][{'hp_ratio': (47, 67), 'class': [('Blood Hunter', 7)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 22, 'stats': {'prof_bonus': 3, 'strength': 13, 'dexterity': 18, 'constitution': 16, 'intelligence': 10, 'wisdom': 18, 'charisma': 14}}][{'monster_id': '1b0df51a-6aad-430a-9dae-935842b13e5e', 'monster_code': 'GFoY1', 'monster_name': 'Gnoll Fang of Yeenoghu', 'level': 4.0}]1{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Blood Hunter', 7)]['Blood Hunter'][(47, 67)][22][3][13][18][16][10][18][14]['Gnoll Fang of Yeenoghu']14.070000000002267470.731318161018141.0000001.00000010000000000004.00
30601665416706-f99ce4ce-246d-468a-a2fd-d8faf7c3ac631.665417e+09['335741543258656549', '188052157020169223', '299624821276559746'][{'hp_ratio': (22, 114), 'class': [('Ranger', 11)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 20, 'stats': {'prof_bonus': 4, 'strength': 12, 'dexterity': 19, 'constitution': 18, 'intelligence': 11, 'wisdom': 12, 'charisma': 8}}, {'hp_ratio': (224, 224), 'class': [('Fighter', 20)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 23, 'stats': {'prof_bonus': 6, 'strength': 13, 'dexterity': 22, 'constitution': 20, 'intelligence': 10, 'wisdom': 20, 'charisma': 10}}, {'hp_ratio': (78, 122), 'class': [('Wizard', 20)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}, 'ac': 14, 'stats': {'prof_bonus': 6, 'strength': 8, 'dexterity': 11, 'constitution': 14, 'intelligence': 20, 'wisdom': 14, 'charisma': 14}}][{'monster_id': 'e2cf43f8-9d9f-48ef-8251-40901d04e870', 'monster_code': 'CB3', 'monster_name': 'Commander Bugs', 'level': 14.0}, {'monster_id': 'c73d0e5a-6467-4339-bcf4-2dd418486f9b', 'monster_code': 'CB4', 'monster_name': 'Commander Bugs', 'level': 14.0}, {'monster_id': 'c1f00eab-d49e-4e7b-a2af-2fa89b5f62b4', 'monster_code': 'CB1', 'monster_name': 'Commander Bugs', 'level': 14.0}, {'monster_id': 'd2e85ec4-e643-417f-adff-1ef1fc4fcbfa', 'monster_code': 'CB5', 'monster_name': 'Commander Bugs', 'level': 14.0}, {'monster_id': 'fc49c8bd-c74a-4145-9fb8-7ee8b0c9ff42', 'monster_code': 'CB2', 'monster_name': 'Commander Bugs', 'level': 14.0}, {'monster_id': '161d0f84-a8a9-4a9c-a68e-1ad3f5b5ae76', 'monster_code': 'TB4', 'monster_name': 'Tiger Bugs', 'level': 8.0}, {'monster_id': 'ec42eb23-4170-476c-9b2e-fb690a091889', 'monster_code': 'TB5', 'monster_name': 'Tiger Bugs', 'level': 8.0}, {'monster_id': '72efe5a6-c343-47d7-ba56-ed8b27e8189c', 'monster_code': 'TB3', 'monster_name': 'Tiger Bugs', 'level': 8.0}, {'monster_id': 'f7ef43a7-9f84-4564-910d-55d5d5df7f76', 'monster_code': 'TB1', 'monster_name': 'Tiger Bugs', 'level': 8.0}, {'monster_id': '94cf00a8-37e8-4c52-99e2-ce28d41ac77d', 'monster_code': 'TB2', 'monster_name': 'Tiger Bugs', 'level': 8.0}, {'monster_id': '23115c29-9d30-4873-b66f-e6e3392237ec', 'monster_code': 'WB1', 'monster_name': 'Warrior Bugs', 'level': 6.0}, {'monster_id': '6ae455ac-ffec-4396-9c84-1f270b02b4ce', 'monster_code': 'WB3', 'monster_name': 'Warrior Bugs', 'level': 6.0}, {'monster_id': 'bb62c317-67c5-481f-bcdf-042254c219ba', 'monster_code': 'WB5', 'monster_name': 'Warrior Bugs', 'level': 6.0}, {'monster_id': '6ab92eb5-6012-4c76-8aff-d1ec0ae13683', 'monster_code': 'WB4', 'monster_name': 'Warrior Bugs', 'level': 6.0}, {'monster_id': '46f54b3c-01d3-470c-b101-750763184a6b', 'monster_code': 'WB2', 'monster_name': 'Warrior Bugs', 'level': 6.0}]3{'1': 8, '2': 6, '3': 5, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}{'1': 8, '2': 6, '3': 6, '4': 3, '5': 3, '6': 2, '7': 2, '8': 1, '9': 1}[('Ranger', 11), ('Fighter', 20), ('Wizard', 20)]['Ranger', 'Fighter', 'Wizard'][(22, 114), (224, 224), (78, 122)][20, 23, 14][4, 6, 6][12, 13, 8][19, 22, 11][18, 20, 14][11, 10, 20][12, 20, 14][8, 10, 14]['Commander Bugs', 'Commander Bugs', 'Commander Bugs', 'Commander Bugs', 'Commander Bugs', 'Tiger Bugs', 'Tiger Bugs', 'Tiger Bugs', 'Tiger Bugs', 'Tiger Bugs', 'Warrior Bugs', 'Warrior Bugs', 'Warrior Bugs', 'Warrior Bugs', 'Warrior Bugs']15140.051865332211574603240.7163352524146320.2000005.0000000100010001000560.01173
30611667499392-a04114b6-b36e-4ab3-8b8d-7aaf28a7445c1.667499e+09['323984029604433629'][{'hp_ratio': (33, 47), 'class': [('Sorcerer', 4), ('Warlock', 3)], 'slots': {'1': 4, '2': 5, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 5, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 3, 'strength': 8, 'dexterity': 14, 'constitution': 14, 'intelligence': 10, 'wisdom': 10, 'charisma': 20}}][{'monster_id': 'b70d6ffb-88ef-476f-9553-61dd02fcf4c5', 'monster_code': 'WW1', 'monster_name': 'Wood Woad', 'level': 5.0}]1{'1': 4, '2': 5, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}{'1': 4, '2': 5, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}[('Sorcerer', 4), ('Warlock', 3)]['Sorcerer', 'Warlock'][(33, 47)][18][3][8][14][14][10][10][20]['Wood Woad']15.074500000001847330.73814141010201.0000001.00000000001000000015.0109
30621661983211-70a7b800-e335-492b-8013-a66d8e7a49dd1.661983e+09['499077502810776283', '196558367709637569', '263880573407494191', '583320224080630228', '278328381340542754'][{'hp_ratio': (80, 167), 'class': [('Sorcerer', 15), ('Bard', 3)], 'slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 1, '7': 1, '8': 1, '9': 1}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 3, '5': 3, '6': 1, '7': 1, '8': 1, '9': 1}, 'ac': 19, 'stats': {'prof_bonus': 6, 'strength': 14, 'dexterity': 10, 'constitution': 20, 'intelligence': 8, 'wisdom': 12, 'charisma': 20}}, {'hp_ratio': None, 'class': [('Fighter', 18)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 19, 'stats': {'prof_bonus': 6, 'strength': 20, 'dexterity': 8, 'constitution': 20, 'intelligence': 9, 'wisdom': 13, 'charisma': 14}}, {'hp_ratio': (118, 180), 'class': [('Fighter', 11), ('Ranger', 3), ('Rogue', 4)], 'slots': {'1': 3, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 3, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 18, 'stats': {'prof_bonus': 6, 'strength': 10, 'dexterity': 20, 'constitution': 18, 'intelligence': 8, 'wisdom': 13, 'charisma': 12}}, {'hp_ratio': None, 'class': [('Blood Hunter', 15)], 'slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 17, 'stats': {'prof_bonus': 5, 'strength': 11, 'dexterity': 20, 'constitution': 12, 'intelligence': 11, 'wisdom': 17, 'charisma': 9}}, {'hp_ratio': (147, 147), 'class': [('Monk', 12), ('Cleric', 6)], 'slots': {'1': 4, '2': 3, '3': 2, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'max_slots': {'1': 4, '2': 3, '3': 3, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}, 'ac': 21, 'stats': {'prof_bonus': 6, 'strength': 10, 'dexterity': 20, 'constitution': 16, 'intelligence': 9, 'wisdom': 20, 'charisma': 13}}][{'monster_id': '1c8dc7b0-f8ba-41c3-80cd-c120dd04961d', 'monster_code': 'FG2', 'monster_name': 'Fire Giant', 'level': 9.0}, {'monster_id': '22821e96-fdd1-49a1-9f51-437e53f53c95', 'monster_code': 'FG1', 'monster_name': 'Fire Giant', 'level': 9.0}, {'monster_id': '1fd7c379-2a91-477f-9485-1a4371a2e962', 'monster_code': 'BE1', 'monster_name': 'Beholder', 'level': 13.0}, {'monster_id': 'c44df959-1cec-4d00-8435-d727a77ef32e', 'monster_code': 'BE3', 'monster_name': 'Beholder', 'level': 13.0}, {'monster_id': '867169cf-dee3-48a6-86e1-bc73dd2257d9', 'monster_code': 'BE2', 'monster_name': 'Beholder', 'level': 13.0}, {'monster_id': '37f2a072-58ff-48ca-8ba6-0a6d22a12137', 'monster_code': 'BE1', 'monster_name': 'Beholder', 'level': 13.0}, {'monster_id': 'e5e48e95-d992-4865-bb3f-4be617c851c0', 'monster_code': 'DA1', 'monster_name': 'Dao', 'level': 11.0}, {'monster_id': '24079558-0beb-4665-9d7c-9681ff2828a8', 'monster_code': 'DA2', 'monster_name': 'Dao', 'level': 11.0}, {'monster_id': '54f6bd09-fb71-48c9-8efe-620eff0eb63f', 'monster_code': 'EE1', 'monster_name': 'Earth Elemental', 'level': 5.0}, {'monster_id': '82e24e25-7eb2-4c60-8332-49442b600002', 'monster_code': 'SN1', 'monster_name': 'Spirit Naga', 'level': 8.0}, {'monster_id': '86397184-09f7-4872-981c-caffc3ab599a', 'monster_code': 'DA2', 'monster_name': 'Dao', 'level': 11.0}, {'monster_id': '22021403-cbc1-41d1-a848-6801d1477af5', 'monster_code': 'DA1', 'monster_name': 'Dao', 'level': 11.0}, {'monster_id': '23aa212c-4b12-46d3-995f-d84b044da86d', 'monster_code': 'DA3', 'monster_name': 'Dao', 'level': 11.0}]5{'1': 11, '2': 6, '3': 5, '4': 3, '5': 3, '6': 1, '7': 1, '8': 1, '9': 1}{'1': 11, '2': 6, '3': 6, '4': 3, '5': 3, '6': 1, '7': 1, '8': 1, '9': 1}[('Sorcerer', 15), ('Bard', 3), ('Fighter', 18), ('Fighter', 11), ('Ranger', 3), ('Rogue', 4), ('Blood Hunter', 15), ('Monk', 12), ('Cleric', 6)]['Sorcerer', 'Bard', 'Fighter', 'Fighter', 'Ranger', 'Rogue', 'Blood Hunter', 'Monk', 'Cleric'][(80, 167), (118, 180), (147, 147)][19, 19, 18, 17, 21][6, 6, 6, 5, 6][14, 20, 10, 11, 10][10, 8, 20, 20, 20][20, 20, 18, 12, 16][8, 9, 8, 11, 9][12, 13, 13, 17, 20][20, 14, 12, 9, 13]['Fire Giant', 'Fire Giant', 'Beholder', 'Beholder', 'Beholder', 'Beholder', 'Dao', 'Dao', 'Earth Elemental', 'Spirit Naga', 'Dao', 'Dao', 'Dao']13138.0871165331111944943450.7296578864575680.3846152.6000001111000101011414.01035